1. 矛盾心理的底层逻辑：生存焦虑与现实压力的冲突
1.1 技术替代威胁的心理机制
人工智能（AI）技术的飞速发展与广泛应用，在极大提升生产效率的同时，也深刻地触及了人类对于职业前景的深层不确定性和生存焦虑。这种焦虑源于个体对“技术替代威胁”的感知，其心理机制可从威胁感知理论（Protection Motivation Theory）的角度进行阐释1。当AI技术迭代加速，具备认知、创造甚至人际互动能力时，传统的人机边界被挑战，工作的知识、任务和社交属性被重新定义，这直接触发了员工对自身能力（competence）、自主性（autonomy）和归属感（relatedness）需求的心理威胁2。
具体而言，AI迭代引发的威胁感知主要体现在以下几个方面：
- 技能贬值风险：随着AI在特定任务中表现出超越人类的精准性和效率，许多原有的专业技能可能迅速失去其市场价值。例如，在医疗影像诊断领域，AI的准确性日益提高，这使得影像技师担忧其专业判断的独占性被削弱3。这种对核心技能未来价值的负面预期，导致了强烈的职业安全感下降。
- 岗位替代概率评估：个体往往会根据AI在不同行业和岗位中的渗透程度，评估自身工作被AI替代的可能性。这种评估并非总是理性，而是受到媒体报道、周围案例以及个人风险偏好等多种因素影响。当这种评估结果趋向高风险时，会产生显著的失业恐惧和生存压力。
- 负面预期的强化路径：心理学研究表明，负面预期一旦形成，就可能通过“社会拒绝预期”修正等机制得到强化。例如，当个体经历或观察到同事因技术进步而面临职业困境时，其自身对被替代的担忧会加剧。即便面对积极的反馈或新机会，根深蒂固的负面预期也难以轻易改变，甚至可能需要经历多次意料之外的积极社会接纳才能有效修正45。这种心理路径导致了一种恶性循环：对AI的恐惧促使个体更加关注负面信息，进一步强化了恐惧，最终可能影响其与AI协作的意愿，进而影响职业可持续性1。有研究指出，信任AI对人机协作有显著正向影响，而威胁感知通过动机路径间接影响协作，这意味着减少威胁感知是促进有效人机协作的关键1。
1.2 工作倦怠的现实驱动因素
与AI替代的生存焦虑并存的，是普遍存在的工作倦怠现象。吊诡的是，AI原本被寄予厚望能够减轻人类工作负担，但在许多现实场景中，它反而成为了加剧工作倦怠的因素，形成了一种“AI未减负反增负”的矛盾。这种现象的根源在于：
- AI辅助下的任务边界模糊与工作量增加：AI工具的引入，常常伴随着工作任务边界的重新定义和扩展。例如，金融工程师在使用AI进行量化分析或风险评估时，不仅需要掌握传统的金融知识和分析方法，还需要投入大量精力学习AI工具的操作、模型调优、结果解读以及将AI输出与传统业务逻辑相整合，甚至需要排查AI可能带来的潜在错误。这种复合型的工作要求，使得单个岗位的工作内容变得更加庞杂，模糊了传统的工作职责界限，导致总工作量的实质性增加。一项针对韩国企业的调查研究发现，AI技术的采纳与员工的工作过载（work overload）之间存在显著的正向关联 6。
- 效率提升带来的目标加码：AI在提升效率方面的显著优势，往往被组织转化为更高的业绩目标和更快的产出要求。例如，在制造业中，AI和自动化产线的引入确实提高了生产效率，但企业可能因此设定更高的生产指标，要求工人更快地完成辅助性任务或监控更多自动化设备。在医疗领域，AI辅助诊断可以大幅缩短诊断时间，但医生可能因此被要求处理更多病例或在有限时间内完成更复杂的分析。这种“效率悖论”使得个体虽然借助AI更快地完成了单项任务，但整体工作负荷并未减轻，反而因为要完成更多的任务或达到更高的标准而感到压力倍增 7。
- 情感劳动需求未降低与“效率陷阱”：AI在处理重复性、数据驱动型任务方面表现卓越，但在需要高度情商、同理心和人际互动的情感劳动领域，其替代能力仍非常有限。例如，在护理行业，AI可以辅助进行数据记录、药物管理甚至初步诊断，但病患的心理慰藉、复杂沟通和情感支持仍需护士亲力亲为。AI的引入有时甚至会加剧情感劳动的强度，因为当重复性任务被AI接管后，人类员工将更多地被分配到需要高度情感投入的工作中，而这些工作往往消耗更大，且难以通过AI“提效”。此外，过度依赖AI可能导致员工批判性思维和决策能力的退化，从而在AI失误或无法应对复杂情境时，反而需要投入更多精力进行纠正和弥补，形成效率陷阱 8。研究也通过心率变异性和眼动追踪等生理指标，揭示了热暴露等高负荷条件下建筑工人心理工作量的增加，这与AI环境下复杂任务对认知负荷的增加有异曲同工之处 9。
- 人机协作的认知负荷：人与AI协作并非总是无缝且轻松的。理解AI的逻辑、与AI系统有效沟通、调试AI可能出现的偏差，以及适应AI的工作节奏，都可能给人类员工带来额外的认知负荷和心理压力。例如，在人机协作的生产环境中，人类工人需要适应与机器人协同工作的节奏，这可能涉及到对机器行为的预测和调整，以及对自身生理和心理状态的持续监控，以确保协作效率和安全性 10。这种持续的注意力投入和适应过程，同样会加剧工作倦怠。
2. 行业场景下的矛盾具象化：从金融到医疗的多维映射
2.1 知识密集型行业：专业身份与技术依赖的博弈
在知识密集型行业中，AI的引入正在重新定义专业人士的角色、职责以及其赖以生存的专业身份。金融工程师和医疗影像技师作为典型代表，其工作内容高度依赖专业知识和决策判断，AI在这些领域扮演的“决策支持”角色，在带来效率提升的同时，也引发了深刻的“被工具替代”身份危机，使其陷入既依赖技术又担忧专业自主权被稀释的复杂矛盾之中。
2.1.1 金融工程师：效率提升与心理安全威胁
金融行业是AI技术应用最为深入的领域之一，尤其是在高频交易、风险管理、量化分析和算法投资等方面。金融工程师通过AI工具能够处理海量数据、识别复杂模式、优化投资组合，从而显著提升工作效率和决策的精准性。然而，这种效率的提升并非没有代价。
一项针对中国和伊朗金融工程师及管理人员的研究揭示了AI对他们心理安全和工作生活平衡的深远影响 11。研究发现：
- 职业不确定性与心理压力：AI驱动的自动化让金融工程师面临更大的压力，要求他们不断学习以跟上技术发展。他们普遍担心自己的工作可能被自动化取代，这种对“饭碗”的担忧直接威胁到他们的心理安全 11。
- 专业身份的模糊与稀释：当AI开始承担部分甚至大部分的分析和决策任务时，金融工程师的核心专业技能——如复杂的数学建模、风险评估直觉——的价值似乎被“下放”给了机器。他们可能会感到自己的专业判断不再是最终和唯一的依据，从而导致专业身份的模糊和认同感的降低。这种感觉介于“AI助手”和“AI操作员”之间，使其难以明确自身在人机协作链条中的最终价值定位。
- 工作-生活界限的模糊：AI带来的高效率和实时性要求，可能导致工作与个人生活之间的界限日益模糊。金融市场瞬息万变，AI工具可以全天候运行，这无形中增加了金融工程师保持持续关注和响应的压力，进一步加剧了工作相关压力 11。
尽管AI在提高效率和决策水平方面潜力巨大，但如何平衡技术进步与员工的心理健康、维护其专业认同感，是金融行业在AI转型中亟需解决的关键问题。
2.1.2 医疗影像技师：诊断辅助与专业自主权的考量
放射医学领域是AI技术最有可能率先实现广泛应用的医学专科之一，尤其是在医学影像的识别、分析和诊断辅助方面 12。AI通过深度学习能够快速准确地识别影像中的病灶，提高诊断效率，减轻影像技师的工作负担。对于影像技师而言，AI工具能够帮助他们更快地筛查出异常、辅助进行量化分析，从而将更多精力投入到复杂病例的诊断和与临床医生的沟通中。
然而，对AI的依赖也引发了影像技师对自身专业自主权和职业未来的深切忧虑：
- “被工具替代”的恐惧：尽管专家普遍认为AI不会完全取代放射科医生和技师，而是会成为他们的“赋能工具” 12。但初期的疑虑和恐惧依然普遍存在，即AI是否会使其核心诊断能力变得冗余，从而削弱其专业地位 1213。这种恐惧尤其体现在那些担心工作量减少或技能被降级的技师群体中 13。
- 专业判断权的稀释：当AI给出高度精确的诊断建议时，影像技师可能会面临“AI判断优先”的压力，即使他们有不同的临床直觉或经验，也可能倾向于采纳AI的结论，以避免潜在的责任。这使得他们的专业判断权在一定程度上被稀释，可能导致他们感觉自己从“诊断者”退化为“AI结果的验证者”。
- 对AI教育和经验的渴望：一项针对欧洲影像技师的调查显示，尽管存在对职业前景的一些担忧，但总体而言，影像技师对AI在医疗中的应用持乐观态度。这种乐观情绪在那些接受过AI教育或有实际AI操作经验的技师中尤为强烈 13。这表明，通过系统的AI教育和实践，可以有效缓解他们的焦虑，帮助他们将AI视为提升专业能力而非替代其工作的工具。
- 伦理与责任边界：AI在诊断中的应用还涉及到伦理和责任的边界问题。当AI出现误诊时，责任应归咎于AI开发者、使用AI的技师还是医生？这些未解决的问题进一步加剧了技师在使用AI时的心理负担。
总而言之，在金融和医疗等知识密集型行业，AI与专业人士的关系呈现出一种复杂的二元性：一方面，AI是提高效率、改善服务的强大工具；另一方面，它也触发了专业人士对自身身份、自主权和未来职业发展的深层焦虑。要实现人机共生，必须在技术部署的同时，关注并解决这些心理和社会层面的挑战。
2.2 劳动密集型行业：体力减负与情感超载的失衡
在劳动密集型行业中，AI和自动化技术的引入被寄予厚望，旨在替代重复性、高强度的体力劳动，从而减轻工人的负担。然而，实际情况往往更为复杂，AI在实现“体力减负”的同时，却可能在人机协作中带来新的“情感超载”与认知负荷，导致工作倦怠的加剧。制造业产线工人和临床护士是这一矛盾的典型缩影。
2.2.1 制造业产线工人：体力减负下的沟通成本与适应压力
制造业是AI和机器人技术最早且最广泛应用的领域之一。自动化生产线和协作机器人（cobots）能够高效完成重复、精确或危险的组装、搬运、检测任务，显著降低了工人的体力消耗和职业伤害风险。例如，通过机械化，某些农业活动的工作速度可以提升数百倍，虽然这与AI并非完全对等，但体现了自动化在体力减负上的潜力14。从表面上看，这似乎应能减少工人的工作倦怠。
然而，对中国制造业的研究表明，AI对就业模式的影响呈现出“U”型关系：短期内AI的替代效应占据主导，长期来看才会出现创造效应，且低技能劳动力更容易被替代15。虽然这种替代效应主要指向岗位数量，但也暗示了AI对现有工人职责的重塑。
AI引入后，产线工人面临新的挑战：
- 人机协作的沟通成本：工人需要学习如何与机器人协作，理解机器的运行逻辑、故障排除和程序调整。这种“沟通”并非简单的指令输入，而是一种复杂的、需要适应的交互过程。当机器人出现故障或需要调整时，工人需要花费额外的时间和精力进行干预，这增加了认知负荷，而非体力负荷的直接替代。
- 持续监控与适应压力：自动化产线虽然减少了直接操作，但增加了对机器运行状态的监控任务。工人可能需要同时管理多台机器，对生产流程的异常进行快速响应。这种持续的警觉性要求和对突发状况的应对，同样会造成精神紧张和心理疲劳。
- 技能升级的压力：为了适应AI和自动化，工人需要掌握新的技能，如机器人编程、数据分析、系统维护等。对于传统体力劳动者而言，这种技能转型是巨大的挑战，可能引发学习焦虑和职业不确定感。有研究指出，技术进步偏向性对总就业有积极影响，且对非技能劳动者有显著正向效应，但对技能劳动者的影响不显著，这可能意味着非技能劳动者需更积极地适应技术进步16。
2.2.2 临床护士：AI辅助与情感劳动的超载
护理行业以其高强度、高压力的工作环境和对情感劳动的高度需求而闻名，护士倦怠问题普遍存在17。AI在护理领域的应用，如智能排班、电子健康记录管理、药物提醒、风险预警甚至辅助诊断，有望减轻护士在重复性任务上的负担18。例如，AI辅助的干预措施已被证明能有效降低护士倦怠，特别是在与患者相关和个人倦怠方面1920。但这些技术进步并未从根本上缓解护理人员的情感劳动超载，有时反而加剧了心理倦怠。
原因在于：
- AI无法替代的情感需求：护理工作的核心在于人际关怀、同情、倾听和建立信任。AI虽然能在数据分析和信息处理方面提供支持，缓解护士的心理健康挑战，但它在提供人类独有的情感连接和共情式互动方面能力有限18。在COVID-19疫情期间，技术如远程医疗虽然保证了服务的连续性，但护士面临的心理压力并未减少，反而凸显了其在情感支持方面的不可或缺性21。
- “效率提升”后的情感工作量增加：当AI接管了部分行政或重复性任务后，护士可能被期望将更多时间投入到直接的患者护理中。这意味着，他们用于处理复杂情感问题、进行深度沟通和提供心理支持的时间和精力需求反而可能增加，导致情感劳动更为密集，从而加剧心理疲劳。
- 人机协作带来的额外压力：护士需要学习和适应新的数字健康工具和AI系统，这本身就是一种负担22。如果系统设计不佳、用户界面不友好，或是培训不足，这些工具反而会成为工作流程中的障碍，耗费护士的宝贵时间和精力，并可能导致新的错误，进一步增加其工作压力和挫败感。
- 工作-生活界限的模糊：在某些情况下，AI系统可能被用于优化排班，但如果优化逻辑过度追求效率而忽视了护士的休息和个人生活需求，反而可能导致工作时间的碎片化或更频繁的班次调整，进一步侵蚀护士的工作-生活平衡。
综上所述，在劳动密集型行业，AI并非是简单地“解放”了工人。它在物理层面的减负可能伴随着认知和情感层面的增负，人机协作的复杂性、技能升级的压力以及AI无法触及的人类情感需求，共同构成了导致工作倦怠的新驱动因素。这要求在推广AI技术时，必须充分考虑其对人类员工心理和情感健康的综合影响。
2.3 教育与培训领域：技能焦虑的代际传递
在教育与培训领域，特别是医学和护理教育中，人工智能的快速发展带来了一场深刻的变革。一方面，AI技术如大型语言模型（LLMs）和AI辅助诊断工具被视为提升学习效率、优化临床决策训练的强大助推器；另一方面，它也引发了学生们对未来职业竞争力和就业前景的深层次焦虑，担忧自身所学技能会被AI取代，形成一种复杂的“技能焦虑的代际传递”现象。
2.3.1 医学生：AI优化学习与职业竞争力担忧
医学生作为未来的医生，他们对AI的态度复杂而矛盾。多项研究表明，医学生普遍认识到AI在医疗领域的巨大潜力，尤其是在提高诊断准确性、减少医疗错误和改善患者护理方面 23242526。他们认为AI可以作为辅助工具，优化学习过程和未来的临床实践。
具体表现为：
- 期待AI优化学习效率：医学生普遍认为AI工具，如大型语言模型，能够通过模拟患者互动和提供结构化反馈来提高临床决策能力 2728。AI能够加速学习、提供最新的医学信息，并帮助他们更好地掌握医学概念，甚至有研究指出，AI在帮助学生理解医学概念方面比传统工具（如书籍和讲座）更有效率 29。例如，AI辅助的病理诊断教学可以提供更直观、更个性化的学习体验。学生们也强烈要求将AI纳入医学课程，以应对未来医疗需求和伦理考量 23243031。
- 对职业竞争力的深层担忧：尽管医学生对AI持积极态度，但“AI将取代医生”的担忧依然存在，尤其是在那些对AI知识和训练不足的学生中 23303233。一项针对医学生的调查显示，37.6%的学生担心AI会取代医生，69.2%的学生担心AI会降低医学的人文关怀属性 23。还有研究发现，医学生担心AI会减少对放射科医生的需求，并降低他们选择放射科作为职业的意愿 32。这种担忧并非空穴来风，例如GPT-4在神经外科专业笔试问题上的表现已超越了医学生和神经外科住院医师 34。这种对“临床决策能力弱化”或“专业地位被AI取代”的恐惧，使得医学生在拥抱AI带来的学习便利的同时，也承受着巨大的职业转型压力。
- 伦理与信任考量：医学生还关注AI带来的伦理问题，如患者隐私泄露、数据安全、信任挑战以及患者-医生关系的变化 23252830。他们认为，在利用AI的同时，必须有严格的监管、保障患者安全和隐私，并解决法律责任问题 33。
2.3.2 护理学生：提高生产力与人际互动挑战
与医学生类似，护理学生对AI的态度也呈现出两面性：一方面认可AI提高生产力和作为促进者的作用；另一方面，他们也对AI可能带来的工作威胁和人际互动方面的影响感到焦虑。
- 认可AI的赋能作用：护理学生普遍认为AI能够提高工作效率、优化患者护理和减少人为错误 2535。他们相信AI可以作为辅助工具，帮助他们在未来的临床实践中更好地完成工作。有研究表明，接受过AI教育模块的学生，其AI素养显著提高，AI焦虑感明显降低 36。生成式AI甚至可以帮助护理学生在模拟训练中减少焦虑、增加准备知识，并与患者故事产生更深的情感联系 37。
- 对人际互动和工作替代的担忧：尽管积极，但护理学生对AI的担忧主要集中在“人情味”的缺失和工作被AI取代的可能性上 2530。他们担心AI会削弱护理工作中至关重要的人际连接和共情能力。此外，尽管一些研究发现，大多数护理学生并不担心AI会取代护士，但他们确实关注AI带来的工作威胁和适应挑战 2538。AI模拟学习虽然能增加学生的满意度，但也可能增加焦虑感，并且在某些技能培训（如乳房自我检查）上，AI辅助模拟不如标准患者模拟有效 39。
总的来说，教育与培训领域的学生们正经历着技能焦虑的代际传递。AI在提供前所未有的学习工具和效率的同时，也迫使他们重新思考自身的专业价值和职业发展路径。未来医学教育的挑战在于，如何在充分利用AI优势的同时，有效缓解学生的焦虑，培养他们与AI协作、而非被AI取代的能力，并确保医学和护理的核心——人文关怀与伦理责任——得以传承和发展。这需要教育体系积极整合AI课程，提供AI实践经验，并引导学生建立“人机共生”的思维模式。
3. AI承担大部分工作后的双重图景：解构恐惧与庆祝的核心议题
3.1 恐惧的合理性：系统性风险与个体适应挑战
当AI承担大部分工作从科幻变为现实时，随之而来的是社会经济结构的深刻变革，以及由此引发的普遍恐惧。这种恐惧并非空穴来风，它根植于对系统性风险的担忧以及个体在适应新范式时面临的巨大挑战。
从经济维度来看，AI承担大部分工作可能带来以下冲击：
- 结构性失业冲击：AI，特别是生成式AI，能够执行传统自动化无法完成的非例行认知任务，这使得其替代劳动力的潜力远超以往的技术变革 40。在制造业中，虽然协作机器人（Cobots）的应用旨在提升效率和灵活性 41，但长期来看，AI对非技能岗位的替代效应尤为显著。AI企业的出现，会吸引劳动力和资本从非AI企业流向AI企业，加速了部分传统行业的岗位缩减。例如，已有研究表明，AI的替代效应在短期内将导致低技能劳动力面临更大的失业风险 42。这意味着大量劳动力将面临失业或被迫转型的困境，从而引发大规模的结构性失业。
- 收入分配失衡加剧：AI的普及将显著提升“技能溢价”效应，即掌握与AI协作或开发AI技术的少数高技能人才将获得更高的报酬，而大部分劳动者则面临收入停滞甚至下降的风险。这种财富向少数AI掌控者和高技能人才集中的趋势，将进一步加剧收入不平等 40。有研究指出，人们往往低估了最富有群体所拥有的收入份额，这可能阻碍了对再分配政策的支持 43。AI在提高企业生产效率的同时，其价格效应可能导致非AI企业收益更多，但如果政府未能通过税收和补贴进行有效调节，劳动力和资本将从AI企业流向非AI企业，最终可能导致劳动收入份额下降和收入不平等加剧 44。
从社会维度来看，恐惧则更多源于对人类存在意义和人际关系的冲击：
- “无意义感”危机：工作不仅仅是谋生手段，更是个体社会身份、价值实现和意义感的重要来源 2。当AI承担大部分重复性、甚至部分创造性工作时，许多人可能会发现自己失去了传统意义上的“工作”，进而面临严重的“无意义感”危机。例如，那些将“努力工作”视为人生重要组成部分的人，在面对AI替代时，其价值观可能受到冲击 45。这种剥夺感可能导致心理健康问题和社会不稳定。
- 人际关系异化：随着人机互动在日常工作和生活中变得日益普遍，部分人际社会联结可能被机器所替代。例如，人型机器人的普及旨在促进人机互动和社会关系，但这种技术能否完全替代人际交流的深度和复杂性仍是疑问 46。当人类更多地与AI系统交互而非与同类交流时，可能会导致社会关系的碎片化和情感连接的减弱。虽然AI在医疗领域有潜力改善人际关系，但如果过度依赖AI，可能削弱医患之间的人文关怀和信任 47484950。
- 个体适应挑战：适应与AI共存的未来并非易事。这不仅需要学习新的技能，更需要改变固有的思维模式和价值观念。例如，一项关于机器人协作意愿的研究表明，不同文化背景下个体对AI的接受度存在差异，集体主义文化可能更倾向于将AI视为自我延伸，而个人主义文化则可能认为AI侵犯了其独特性和自主性 51。对于那些对新技术接受度较低，特别是老年群体而言，适应AI主导的社会将是一个巨大的挑战。他们可能在学习新技能、理解新系统和融入新社会结构方面面临更多困难，导致数字鸿沟和代际隔阂的加剧。
综上所述，AI承担大部分工作后，其带来的经济失衡和社会冲击是真实的，由此产生的恐惧是具有合理性的。我们需要正视这些潜在的负面影响，并积极寻求解决方案，以减轻其对社会和个体的冲击。
3.2 庆祝的可能性：效率解放与价值重构契机
尽管AI承担大部分工作可能带来诸多担忧，但我们同样应看到其带来的巨大机遇，即效率的解放和人类价值的重构。这种积极的未来图景，是值得我们庆祝的，因为它预示着人类将有更多机会从事更有意义、更具创造性、更符合人类本性的活动。
3.2.1 工作性质的转型：从重复到创造，从任务到关怀
AI在处理重复性、模式化任务方面的卓越能力，将人类从繁琐、低价值的工作中解放出来，从而实现工作性质的根本性转型：
- 人类转向创造性劳动：当AI高效完成数据分析、报告撰写、图像识别等任务后，人类可以投入更多精力进行概念创新、策略制定、艺术创作、科学研究等需要深层理解、批判性思维和高度想象力的工作。例如，在医疗领域，AI科学家能够整合AI模型和生物医学工具，辅助分析大量数据，并导航假设空间，从而“赋能生物医学研究”52。医生可以投入更多时间进行疑难病例研究、新疗法开发或参与医学教育，而不再被基础诊断工作所困扰。文本生成AI能提高人类的创造性生产力，并在短时间内产出更高价值的作品，表明AI能够作为辅助工具而非替代者，赋能人类创新53。
- 高情感需求服务成为核心：AI虽然在认知任务上表现出色，但在处理复杂人类情感、提供同情与共情、进行深度人际互动方面仍显不足。因此，当AI接管大部分技术性任务后，人类的价值将更多地体现在那些需要高情感投入的服务领域，如心理咨询、教育指导、老年护理、痴呆患者陪伴等。AI可以协助进行基础监测或信息整理，但人与人之间的温暖、理解和支持是无法被机器完全替代的。例如，老年机器人陪伴案例表明，AI在缓解孤独感方面具有潜力，但其效果仍需在社会和临床干预中整合，且需要警惕“反治疗性转移”等风险54。这正是人类在未来社会中，其“人”的价值——即情感与关怀——将被前所未有地强调和重视。
3.2.2 个体层面：工作-生活边界的重构与自我实现空间的拓展
AI的普及不仅改变了工作内容，还将对个体的生活方式和自我实现产生深远影响：
- “工作-生活”边界的重构：AI工具的智能调度和自动化能力，有助于优化资源分配和时间管理，从而为个体提供更大的工作弹性。例如，在护理行业，智能排班系统可以根据护士的偏好、技能和工作负荷进行优化，更好地平衡工作与生活。一项针对医疗行业的研究发现，环境AI文书助理显著降低了医生的任务负荷和职业倦怠，并提升了可用性，从而改善了医生工作与生活的平衡5556。AI在教育机构的应用也显示出其对女性教职员工工作与生活平衡的积极影响57。这意味着，工作可能不再是僵化的朝九晚五，而是更加灵活、自主的模式，使个体能够更好地整合职业发展与个人生活需求。
- 自我实现空间的拓展：当重复性任务被AI承担后，人类将有更多时间和精力去探索个人兴趣、学习新知识、发展新技能，从而拓展自我实现的广度和深度。AI工具能够赋能小众职业的发展，通过提供个性化的学习资源和工具，降低进入门槛，激发更多创新活力。例如，AI在教育中的应用被视为提升学生综合发展的重要手段，有助于他们在生存、成长和自我实现阶段的职业发展58。这种解放将鼓励人们从“谋生”向“乐生”转变，追求更高级的精神需求，实现个体的全面发展。
综合来看，AI承担大部分工作后的未来，并非只有失业的阴影，更有解放的曙光。关键在于如何引导技术发展，制定合理政策，以及社会和个体如何积极适应和利用AI带来的变革，从而将“恐惧”转化为“庆祝”的契机。这要求我们超越眼前的焦虑，以更宏大的视角审视人机共生的未来，积极构建一个以人为本、智能辅助的社会体系。
4. 从矛盾到共生：构建适应性社会系统的路径
面对AI带来的替代焦虑和工作倦怠的双重矛盾，社会系统必须积极探索并构建适应性路径，以实现人机共生而非对立。这需要从制度、组织和个体三个层面协同发力，设计前瞻性政策、优化技术应用与管理模式、并引导个体提升能力与调适心态。最终目标是化解AI带来的潜在风险，最大限度地发挥其作为生产力工具的优势，并促进人类社会的持续进步与福祉。
4.1 制度层面：风险缓冲与权益保障
为应对AI广泛应用可能引发的社会经济冲击，制度层面的设计至关重要。这包括构建有效的风险缓冲机制，以及为受影响群体提供全面的权益保障，确保社会转型的公平性和可持续性。
建立AI替代预警与技能转换基金：
随着AI技术的快速发展，某些岗位被替代将不可避免。政府应建立一套动态的AI替代预警机制，通过对不同行业、不同技能水平岗位的AI渗透率和替代潜力的持续监测与评估，提前识别高风险职业群体。基于此预警，设立专项的“AI技能转换基金”，为面临失业或技能贬值的劳动者提供免费或低成本的再培训和职业转型支持。该基金的资金来源可以考虑通过对AI技术收益企业征收“机器人税”或从AI驱动的生产效率提升中提取部分利润。参考制造业技术进步对就业影响的阈值研究，当AI替代率达到特定阈值时，应立即启动相应的干预措施，确保劳动者能够顺利过渡到新兴岗位或具备与AI协作的新技能。例如，中国已有研究表明，AI对就业结构的影响具有非线性特征，能促进就业结构优化升级59，但前提是需要政策引导以避免短期内的负面冲击。完善弹性社会保障体系：
AI驱动的就业模式可能催生更多非标准就业形态（如平台零工、远程协作等），这些就业形式的劳动者往往缺乏传统雇佣关系下的社会保障。因此，有必要对现有的社会保障体系进行改革和完善，使其能够覆盖所有就业类型，包括疾病福利、失业保险、养老金等，确保劳动者在职业转型或收入波动期间的基本生活保障60616263。例如，应探讨建立全民基本收入（UBI）的可行性，以应对AI大规模替代工作岗位后可能出现的普遍失业问题，为公民提供维持基本生活的底线保障，从而缓解因技术进步带来的生存焦虑。制定人机协作伦理框架：
AI在医疗、金融等关键领域的应用，涉及到复杂的伦理和法律问题。例如，当医疗AI出现误诊时，责任应如何界定？患者的隐私数据如何在使用AI时得到保护？为了规范AI的使用，维护人类尊严和权利，必须制定一套全面、清晰的人机协作伦理框架和法律法规。这包括：- 透明度原则：AI系统的决策过程应尽可能透明，允许人类理解其判断依据，而非“黑箱操作”。
- 可问责性原则：明确AI系统失误时的责任主体，确保有纠正和赔偿机制。
- 人类中心原则：强调AI应作为辅助工具，服务于人类福祉，而非取代人类的最终决策权和核心价值。特别是在医疗领域，应明确AI的错误率可接受标准，并强制要求人类医生进行最终复核，以保障患者安全和医疗质量。
- 数据隐私与安全：制定严格的数据保护法规，确保AI在使用和处理个人数据时符合伦理标准，避免滥用和泄露。
通过这些制度层面的改革与创新，社会能够更好地缓冲AI技术带来的冲击，保障劳动者的基本权益，并为构建一个人机和谐共生的未来奠定坚实基础。
4.2 组织层面：技术设计与管理创新
在AI时代，企业作为技术应用的直接主体和员工的主要雇主，其在技术设计和管理创新方面的角色至关重要。企业需要超越单纯追求效率的旧思维，转变为积极构建人机共生工作环境的先行者。这要求企业开发“增能型”AI工具、优化人机分工流程，并构建支持性的组织文化。
开发“增能型”AI工具而非“替代型”AI：
企业在设计和部署AI工具时，应将重心放在“赋能”（augmentation）而非“替代”（automation）上。这意味着AI应被视为人类能力的延伸和补充，而非削弱或取代人类智能。- 聚焦辅助与决策支持：例如，在护理领域，AI应着重于风险预警、信息整合和日常管理任务的自动化，而非直接替代护士进行决策或情感沟通。有研究指出，AI驱动的决策支持系统能够有效协助护士进行压疮和失禁相关皮炎的鉴别诊断，通过整合护理科学证据和临床实践数据，显著提高了评估的准确性 6465。此类AI工具通过提供更精确的诊断支持，减轻了护士的认知负担，使其能将更多精力投入到更复杂的护理判断和患者关怀中。另一个例子是，AI在糖尿病视网膜病变筛查中辅助全科医生，使医生能专注于评估AI结果和与患者沟通，而非完全替代诊断过程 66。
- 提升工作效率与体验： AI工具的设计应充分考虑用户体验，确保其直观易用，减少学习曲线和操作障碍。有效的用户参与到AI工具的开发和实施中可以增强用户接受度 67。良好的用户界面设计，能够提高员工对AI的接受度和使用满意度，将其视为有益的助手而非负担 68。
优化人机分工流程，实现优势互补：
AI与人类各有所长，企业应深入分析任务特性，设计出能够最大化双方优势的人机协作流程。- AI处理重复性、数据密集型任务：将大量数据分析、模式识别、信息筛选等重复性、耗时性任务交给AI。例如，放射科的AI系统可以进行影像的初步筛查、病灶定位和量化分析，显著提高效率 697071。AI能够快速精准地检测出颅内动脉瘤，并提升临床医生诊断性能，减少阅读时间 72。类似地，AI作为“第二判读人”，可以提高放射科医生和非放射科医生在胸片中肺结节的检出率和定位准确性 73。
- 人类专注于复杂决策、创造性与情感交互：将需要临床经验、道德判断、战略规划、创新思维和情感共鸣的任务留给人类。例如，在放射科，AI在医学影像分析中扮演着重要角色，在图像分割、计算机辅助诊断、预测分析和工作流程优化方面发挥着基础性作用 69。AI驱动的3D重建系统可以显著提高肺外科手术规划中解剖变异识别的准确性，但最终手术方案的确定仍需经验丰富的胸外科医生进行决策 74。这种分工模式使得人类能够专注于更高价值、更具挑战性的工作，从而提升职业满意度和成就感。
构建支持性组织文化与持续学习机制：
企业文化的建设在AI转型中扮演着核心角色。建立一个鼓励学习、接纳变革、强调心理安全的文化氛围至关重要 7576。- 提供持续的AI培训与心理辅导：企业应为员工提供系统的AI知识和技能培训，帮助他们理解AI的工作原理、掌握AI工具的使用方法，并培养与AI协作的思维模式。例如，针对放射科住院医师的AI培训研讨会可以显著提升他们对AI概念的理解和信心 77。同时，考虑到员工可能存在的AI焦虑和职业不确定性，企业应提供心理辅导和职业咨询服务，帮助员工应对转型期的压力，重塑职业信心。
- 营造心理安全和创新文化：鼓励员工尝试新工具、探索新方法，并允许在学习和适应过程中出现错误。一个心理安全的组织环境能够激发员工的创新潜力，促进他们主动拥抱AI技术。企业应推广“学习型组织”理念，让员工认识到终身学习是适应AI时代的必然要求，并通过内部知识分享和最佳实践推广，促进全员共同进步。
通过上述技术设计与管理创新，企业不仅能够有效整合AI技术，提升运营效率，更重要的是，能够促进员工与AI的协同发展，将“AI替代焦虑”转化为“人机共生机遇”，实现可持续的增长和发展。
4.3 个体层面：能力升级与心理调适
在AI时代，个体不再是被动接受技术变革的客体，而是积极主动应对、与AI共生发展的主体。面对AI替代焦虑与工作倦怠，个人层面的能力升级与心理调适成为关键，旨在帮助个体掌握与AI协作的技能，调整心态，重塑职业意义。
主动学习AI协作技能：
AI并非要取代人类，而是赋能人类，使其能够完成更复杂、更具创造性的工作。因此，主动学习与AI协作的技能，是个人适应AI时代的根本要求。这包括：- 掌握AI工具的使用与评估方法：个体应积极学习本行业内AI工具的操作方法、功能边界及其输出结果的评估能力。例如，医学学生应掌握AI辅助诊断工具的使用，理解其诊断逻辑、局限性及可能的偏见，并能够对AI的诊断建议进行批判性评估和临床验证 78。一项针对医疗学生的调查显示，医学生普遍认为AI在诊断和治疗中具有重要作用，并愿意在医疗培训中学习AI技能 23。另一项研究则强调，医学生需要学习AI知识和技能来有效地利用AI工具 79。
- 数据素养与算法理解：理解AI背后的数据原理和算法逻辑，有助于个体更好地与AI交互，识别潜在的风险和局限。
- 跨领域融合能力：AI的发展催生了大量跨学科、跨领域的新岗位。具备将自身专业知识与AI技术相结合的能力，将成为未来职场的核心竞争力。
培养“人机共生”思维：
从心理层面，个体需要将AI视为协作伙伴而非竞争对手，培养一种“人机共生”的思维模式。- 接受AI作为辅助而非对手：这种思维转变至关重要。例如，护士在使用AI工具时，应将其视为能够减轻重复性劳动、提供决策支持的助手，而非潜在的职业威胁。研究显示，护理学生在使用AI（如ChatGPT）进行护理过程培训后，其解决问题的能力、对AI的积极态度以及护理过程能力均显著提高，满意度也更高 80。这表明，通过积极的培训和实践，可以有效提升个体对AI的接受度和信心。
- 信任与批判性思维并存：在信任AI带来便利的同时，保持批判性思维，不盲目依赖AI的输出，尤其是在涉及复杂判断和伦理决策的场景中，充分发挥人类的独特优势。
重构工作意义认知：
在AI可能承担大部分工作的未来，工作不再仅仅是任务的完成，而是价值的创造和自我实现的途径。个体需要重新审视工作的意义，从“任务完成”转向“价值创造”。- 从重复性劳动中解放，追求高阶价值：当AI接管重复性、低价值的任务后，个体可以将精力聚焦于需要创造力、同理心、战略思维和复杂问题解决能力的高阶工作。这将促使人们从传统意义上的“劳动者”向“创造者”转变。
- 关注人际互动与情感价值：在AI无法替代的情感劳动和人际互动领域，人类的价值将更加凸显。例如，医学生和护士学生对AI可能削弱医学和护理中的人文关怀表示担忧 23。这恰恰提醒我们，在AI时代，医护人员在患者教育、心理支持和人际沟通中的作用将更为关键，这些正是AI难以替代的、具有深刻意义的价值创造。
- 终身学习与适应性心态：认识到职业生涯将是持续学习和不断适应的过程，保持开放的心态，拥抱变化，将挑战视为成长和自我提升的机遇。
通过在个体层面的能力升级和心理调适，能够有效缓解对AI的焦虑，增强与AI协作的能力，并找到在智能化社会中实现个人价值的新途径。这不仅是个体的责任，也需要教育体系、企业和社会提供相应的支持和引导。
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参考文献
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Ngoc-Hong Duong, Hoang Ngoc Hung, Nguyen Hoai An, et al.
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This study examines how employees' perceptions of AI trust and AI threat shape their collaboration with AI and subsequent career sustainability, grounded in Protection Motivation Theory and Person-Environment Fit Theory. Using data collected from 532 employees through an online survey, the relationships among constructs were analyzed using PLS-SEM. Results reveal that trust in AI exerts a positive and significant influence on employee-AI collaboration, while threat perception indirectly affects collaboration through motivational pathways. Furthermore, protean career orientation positively moderates the link between AI trust and collaboration, indicating that self-directed career attitudes strengthen adaptive engagement with AI systems. These findings underscore that approach and avoidance motivation play distinct roles in shaping collaboration outcomes, which in turn enhance various facets of career sustainability. The study contributes to theory by integrating cognitive appraisal and motivation perspectives to explain human-AI dynamics and provides managerial implications for fostering effective AI integration and sustainable career development in the digital workplace.
2GenAI and the psychology of work.PubMed
Erik Hermann, Stefano Puntoni, Carey K Morewedge
Trends Cogn Sci. 2025 Sep;29(9):802-813. doi: 10.1016/j.tics.2025.04.009. Epub 2025 May 8.
Work is a central source of identity and meaning. The rapid and widespread adoption of generative artificial intelligence (GenAI) is reshaping workplaces. Unlike previous technologies, GenAI can demonstrate cognitive, creative, and interpersonal capabilities that challenge traditional human-machine boundaries and redefine the knowledge, task, and social characteristics of work. GenAI can benefit workers by enhancing their productivity and performance. It can also psychologically threaten workers' needs for competence, autonomy, and relatedness, which can initiate five coping strategies to mitigate these threats. We unpack the effects of GenAI on work and workers, show the importance of addressing its potential psychological threats, and explain how to foster human-centered workplaces that balance the benefits and risks of GenAI.
3The future of radiology and radiologists: AI is pivotal but not the only change afoot.PubMed
Edmund M Weisberg, Elliot K Fishman
J Med Imaging Radiat Sci. 2024 Dec;55(4):101377. doi: 10.1016/j.jmir.2024.02.002. Epub 2024 Feb 24.
Uncertainty regarding the future of radiologists is largely driven by the emergence of artificial intelligence (AI). If AI succeeds, will radiologists continue to monopolize imaging services? As AI accuracy progresses with alacrity, radiology reads will be excellent. Some articles show that AI can make non-radiologists experts. However, eminent figures within AI development have expressed concerns over its possible adverse uses. Bad actors, not bad AI, may account for a future in which AI is not as successful as we might hope and, as some fear, even pernicious. More relevant to current predictions over the course of AI in medicine, and radiology in particular, is how the evolution of AI is often seen in a vacuum. We cannot predict the future with certainty. But as we contemplate the potential impact of AI in radiology, we should remember that radiology does not exist in a vacuum; while AI is changing, so is everything else. The medical system, not to mention the world's population, has been severely impacted by the global COVID-19 pandemic and numerous experts expect future worldwide pandemics. We cannot predict the condition of the healthcare system in two decades but may assume that radiology will likely remain critical in any future medical practice. For now, we should responsibly use all tools at our disposal (including AI) to make ourselves as indispensable as possible. Our best chances of remaining relevant and instrumental to patient care will likely hinge on our ability to lead the changes rather than be passively impacted by them.
4How to modify expectations of social rejection? An experimental study using a false-feedback paradigm.PubMed
Lukas Kirchner, Tobias Kube, Lisa D'Astolfo, et al.
J Behav Ther Exp Psychiatry. 2023 Dec;81:101859. doi: 10.1016/j.jbtep.2023.101859. Epub 2023 Apr 27.
BACKGROUND AND OBJECTIVES: Negative expectations (NEs) are fundamental to various mental disorders. Finding ways to modulate NEs would help to improve clinical treatment. The present study investigated how previously formed expectations of social rejection are revised in the context of novel positive social experiences, and whether their revision can be modulated by differentially shifting participants' attentional focus. METHODS: Our sample of 124 healthy participants was randomly assigned to four experimental conditions and received manipulated social feedback in multiple alleged webcam conferences. All groups went through three experimental phases that began with predominantly negative social feedback, then either transitioned to predominantly positive social feedback or continued to predominantly negative social feedback, and ultimately transitioning to a phase with no explicit social feedback. The experimental conditions differed in what they were instructed to focus on when receiving positive social feedback. RESULTS: Receiving novel positive social feedback led to substantial changes in social expectations, but this effect was not modulated by the instructions the participants were given. Descriptive trends revealed that both instructions improved NE modification, although this effect was not robust to extinction in one condition. LIMITATIONS: To prevent our cover story from being compromised, we could not perform an immediate manipulation check of the instructions given. Nevertheless, some of the sample seemed suspicious about the cover story. CONCLUSION: Our results suggest that established expectations of social rejection can be revised when unexpectedly experiencing social acceptance. Nevertheless, more research is needed on potential instructions that could be used to optimize the modification of NEs.
5No1LikesU! - A Pilot Study on an Ecologically Valid and Highly Standardised Experimental Paradigm to Investigate Social Rejection Expectations and Their Modification.PubMed
Lisa D'Astolfo, Lukas Kirchner, Winfried Rief
Clin Psychol Eur. 2020 Jun 30;2(2):e2997. doi: 10.32872/cpe.v2i2.2997. eCollection 2020 Jun.
BACKGROUND: Dysfunctional expectations have been suggested as core features in the development and maintenance of mental disorders. Thus, preventing development and promoting modification of dysfunctional expectations through intervention might improve clinical treatment. While there are well-established experimental procedures to investigate the acquisition and modification of dysfunctional performance expectations in major depression, paradigms for investigating other important types of dysfunctional expectations (e.g. social rejection expectations) are currently lacking. We introduce an innovative associative learning paradigm, which can be used to investigate the development, maintenance, and modification of social rejection expectations. METHOD: A pilot sample of 28 healthy participants experienced manipulated social feedback after answering personal questions in supposed webcam conferences. While participants repeatedly received social rejection feedback in a first phase, differential feedback was given in a second phase (social rejection vs. social appreciation). In a third phase, explicit social feedback was omitted. RESULTS: Participants developed social rejection expectations in the first phase. For the second phase, we found an interaction effect of experimental condition; i.e. participants adjusted their expectations according to the differential social feedback. In the third phase, learned social expectations remained stable in accordance to the social feedback in the second phase. CONCLUSION: Results indicate that the paradigm can be used to investigate the development, maintenance, and modification of social rejection expectations in healthy participants. This offers broad applications to explore the differential acquisition and modification of social rejection expectations in healthy vs. clinical samples. Further, the paradigm might be used to investigate therapeutic strategies to facilitate expectation change.
6The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.PubMed
Byung-Jik Kim, Min-Jik Kim
J Environ Manage. 2025 Apr;380:125102. doi: 10.1016/j.jenvman.2025.125102. Epub 2025 Mar 24.
This study examines the complex relationships among artificial intelligence (AI) adoption in organizations, employee work overload, and pro-environmental behavior at work (PEBW), while examining the moderating role of self-efficacy in AI learning. Drawing on several theories, we developed and tested a moderated mediation model utilizing a 3-wave time-lagged survey of 416 employees from diverse South Korean corporations. Our findings reveal that the link between AI adoption and PEBW is fully mediated by work overload, with AI adoption positively influencing work overload, which in turn negatively affects PEBW. Importantly, self-efficacy in AI learning moderates the AI adoption-work overload link, such that the positive influence is weaker for members with higher levels of self-efficacy. These results highlight the unintended consequences of AI adoption on environmental behaviors and underscore the significance of individual differences in shaping responses to technological change. The current research contributes to the literature by elucidating the mechanisms through which AI adoption influences PEBW and by identifying factors that can mitigate potential negative effects. The findings offer meaningful perspectives for organizations aiming to balance technological advancement with environmental sustainability goals, emphasizing the need for strategies that enhance members' self-efficacy in AI learning and manage workload effectively. This paper advances our knowledge of the complex interplay between technological adoption, work experiences, and pro-environmental behaviors in contemporary organizational settings.
7The AI Efficiency Paradox: Reclaiming Quality Patient Care in an Era of Optimization.PubMed
Julian Michael Burwell
J Med Syst. 2025 Apr 17;49(1):49. doi: 10.1007/s10916-025-02183-2.
We examine how artificial intelligence (AI) integration in healthcare may create an "efficiency paradox" where technologies designed to reduce workload can instead generate new layers of inefficiency. We argue that AI implementation strategies prioritizing efficiency metrics over meaningful patient interactions risk undermining care quality. A framework is proposed for evaluating AI adoption that balances technological optimization with perseveration of the physician-patient relationship.
8Paradox of AI in Higher Education: Qualitative Inquiry Into AI Dependency Among Educators in Palestine.PubMed
Anas Ali Alhur, Zuheir N Khlaif, Bilal Hamamra, et al.
JMIR Med Educ. 2025 Sep 15;11:e74947. doi: 10.2196/74947.
BACKGROUND: Artificial intelligence (AI) is increasingly embedded in medical education, providing benefits in instructional design, content creation, and administrative efficiency. Tools like ChatGPT are reshaping training and teaching practices in digital health. However, concerns about faculty overreliance highlight risks to pedagogical autonomy, cognitive engagement, and ethics. Despite global interest, there is limited empirical research on AI dependency among medical educators, particularly in underrepresented regions like the Global South. OBJECTIVE: This study focused on Palestine and aimed to (1) identify factors contributing to AI dependency among medical educators, (2) assess its impact on teaching autonomy, decision-making, and professional identity, and (3) propose strategies for sustainable and responsible AI integration in digital medical education. METHODS: A qualitative research design was used, using semistructured interviews (n=22) and focus group discussions (n=24) involving 46 medical educators from nursing, pharmacy, medicine, optometry, and dental sciences. Thematic analysis, supported by NVivo (QSR International), was conducted on 15.5 hours of transcribed data. Participants varied in their frequency of AI use: 45.7% (21/46) used AI daily, 30.4% (14/46) weekly, and 15.2% (7/46) monthly. RESULTS: In total, 5 major themes were identified as drivers of AI dependency: institutional workload (reported by >80% [37/46] of participants), low academic confidence (noted by 28/46, 60%), and perfectionism-related stress (23/46, 50%). The following 6 broad consequences of AI overreliance were identified: Skills Atrophy (reported by 89% [41/46]): educators reported reduced critical thinking, scientific writing, and decision-making abilities. Pedagogical erosion (35/46, 76%): decreased student interaction and reduced teaching innovation. Motivational decline (31/46, 67%): increased procrastination and reduced intrinsic motivation. Ethical risks (24/46, 52%): concerns about plagiarism and overuse of AI-generated content. Social fragmentation (22/46, 48%): diminished peer collaboration and mentorship. Creativity suppression (20/46, 43%): reliance on AI for content generation diluted instructional originality., Strategies reported by participants to address these issues included establishing boundaries for AI use (n=41), fostering hybrid intelligence (n=37), and integrating AI literacy into teaching practices (n=39). CONCLUSIONS: While AI tools can enhance digital health instruction, unchecked reliance risks eroding essential clinician competencies. This study identifies cognitive, pedagogical, and ethical consequences of AI overuse in medical education and highlights the need for AI literacy, professional development, and ethical frameworks to ensure responsible and balanced integration.
9Monitoring Construction Workers' Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers.PubMed
Shiyi He, Dongsheng Qi, Enkai Guo, et al.
Sensors (Basel). 2025 Apr 9;25(8):2377. doi: 10.3390/s25082377.
Monitoring the mental workload of construction workers is effective in detecting risky subjects because cognitive overload may threaten their safety. This study aimed to measure workers' mental workload caused by heat exposure using heart rate variability (HRV) and eye movement features. Inexperienced pipe workers ( = 30) were invited to perform an installation task in a normothermic (26 °C, 50% RH) and a hyperthermic (33 °C, 50% RH) condition. Their HRV and eye movement features were recorded as the inputs of training models classifying mental workload between the two thermal conditions, using supervised machine learning algorithms, including Support Vector Machines (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF). The results show that applying eight HRV features through the KNN algorithm could obtain the highest classification accuracy of 90.00% (Recall = 0.933, Precision = 0.875, F1 = 0.903, AUC = 0.887). This study could provide a new perspective for monitoring the mental workload of construction workers, and it could also provide a feasible approach for the construction industry to monitor workers' mental workload in hot conditions.
10Learning performance and physiological feedback-based evaluation for human-robot collaboration.PubMed
Chiuhsiang Joe Lin, Rio Prasetyo Lukodono
Appl Ergon. 2025 Apr;124:104425. doi: 10.1016/j.apergo.2024.104425. Epub 2024 Nov 28.
The development of Industry 4.0 has resulted in tremendous transformations in the manufacturing sector to supplement the human workforce through collaboration with robots. This emphasis on a human-centered approach is a vital aspect in promoting resilience within manufacturing operations. In response, humans need to adjust to new working conditions, including sharing areas with no apparent separations and with simultaneous actions that might affect performance. At the same time, wearable technologies and applications with the potential to gather detailed and accurate human physiological data are growing rapidly. These data lead to a better understanding of evaluating human performance while considering multiple factors in human-robot collaboration. This study uses an approach for assessing human performance in human-robot collaboration. The assessment scenario necessitates understanding of how humans perceive collaborative work based on several indicators, such as perceptions of workload, performance, and physiological feedback. The participants were evaluated for around 120 min. The results showed that human performance improved as the number of repetitions increased, and the learning performance value was 92%. Other physiological indicators also exhibited decreasing values as the human performance tended to increase. The findings can help the industry to evaluate human performance based on workload, performance, and physiological feedback information. The implication of this assessment can serve as a foundation for enhancing resilience by refining work systems that are adaptable to humans without compromising performance.
11How can AI-integrated applications affect the financial engineers' psychological safety and work-life balance: Chinese and Iranian financial engineers and administrators' perspectives.PubMed
Ke Gao, Alireza Zamanpour
BMC Psychol. 2024 Oct 15;12(1):555. doi: 10.1186/s40359-024-02041-9.
BACKGROUND: The integration of AI in finance has significantly reshaped the role of financial engineers, improving efficiency and decision-making. However, it also affects psychological safety and work-life balance. Financial engineers face increased pressure to keep up with evolving technologies, fear of job displacement due to automation, and blurred boundaries between work and personal life. Exploring the link between AI applications, psychological well-being, and work-life balance is crucial for optimizing individual performance and organizational success, ensuring a sustainable and supportive work environment. OBJECTIVES: This qualitative study investigates how AI-integrated finance applications influence financial engineers' psychological safety and work-life balance. By exploring financial engineers' lived experiences and perceptions, the study seeks to provide insights into the human implications of AI adoption in finance. METHODOLOGY: The study utilized qualitative research methods, specifically thematic analysis, to examine data from 20 informants selected through theoretical sampling. Thematic analysis techniques were employed to identify recurring patterns, themes, and meanings within the data, allowing for a rich exploration of the research questions. FINDINGS: Data analysis revealed several themes related to the impact of AI-integrated applications on financial engineers' psychological safety and work-life balance. These themes include the perception of job security, the role of automation in workload management, and the implications of AI for professional identity and job satisfaction. CONCLUSIONS: This study's findings highlight the multifaceted effects of AI integration in finance, shedding light on the opportunities and challenges it presents for financial engineers. While AI offers potential benefits such as increased efficiency and productivity, it raises concerns about job security and work-related stress. Overall, the study underscores the importance of considering the human implications of AI adoption in finance and calls for proactive measures to support the well-being of financial professionals in an AI-driven environment.
12Navigating the AI revolution: will radiology sink or soar?PubMed
Heinz-Peter Schlemmer
Jpn J Radiol. 2025 Oct;43(10):1628-1633. doi: 10.1007/s11604-025-01810-9. Epub 2025 Jul 31.
The rapid acceleration of digital transformation and artificial intelligence (AI) is fundamentally reshaping medicine. Much like previous technological revolutions, AI-driven by advances in computer technology and software including machine learning, computer vision, and generative models-is redefining cognitive work in healthcare. Radiology, as one of the first fully digitized medical specialties, is at the forefront of this transformation. AI is automating workflows, enhancing image acquisition and interpretation, and improving diagnostic precision, which collectively boost efficiency, reduce costs, and elevate patient care. Global data networks and AI-powered platforms are enabling borderless collaboration, empowering radiologists to focus on complex decision-making and patient interaction. Despite these profound opportunities, widespread AI adoption in radiology remains limited, often confined to specific use cases, such as chest, neuro, and musculoskeletal imaging. Concerns persist regarding transparency, explainability, and the ethical use of AI systems, while unresolved questions about workload, liability, and reimbursement present additional hurdles. Psychological and cultural barriers, including fears of job displacement and diminished professional autonomy, also slow acceptance. However, history shows that disruptive innovations often encounter initial resistance. Just as the discovery of X-rays over a century ago ushered in a new era, today, digitalization and artificial intelligence will drive another paradigm shift-this time through cognitive automation. To realize AI's full potential, radiologists must maintain clinical oversight and safeguard their professional identity, viewing AI as a supportive tool rather than a threat. Embracing AI will allow radiologists to elevate their profession, enhance interdisciplinary collaboration, and help shape the future of medicine. Achieving this vision requires not only technological readiness but also early integration of AI education into medical training. Ultimately, radiology will not be replaced by AI, but by radiologists who effectively harness its capabilities.
13R-AI-diographers: a European survey on perceived impact of AI on professional identity, careers, and radiographers' roles.PubMed
Nikolaos Stogiannos, Gemma Walsh, Benard Ohene-Botwe, et al.
Insights Imaging. 2025 Feb 17;16(1):43. doi: 10.1186/s13244-025-01918-6.
OBJECTIVES: Radiographers use advanced medical imaging and radiotherapy (MIRT) equipment. They are also a digitally mature and digitally resilient workforce in healthcare. Artificial intelligence is already changing their clinical practice and roles in data acquisition, post-processing, and workflow management. It is therefore vital to understand the impact of AI on the careers, roles and professional identity of radiographers, as key stakeholders of the digital transformation of healthcare within the medical imaging ecosystem. METHODS: A European radiographer survey, endorsed by the European Federation of Radiographer Societies (EFRS), was distributed online. It was piloted with twelve radiographers and translated into eight languages. Although this study included both qualitative and quantitative results, this paper emphasises the quantitative aspect. RESULTS: A total of 2206 European radiographers have responded from 37 different countries. Despite some concerns around workforce deskilling, future professional identity, and job prospects, participants showed overall optimistic views about the use of AI in healthcare. This was particularly strong for those with prior AI education (mean: 2.15 vs. 1.89; p-value: < 0.001), hands-on experience with AI (correlation: 0.047; p-value: 0.038), from countries with higher digital literacy (mean: 2.00 vs.1.93; p-value: 0.027) and a higher academic level of radiography education (mean: 3.28 vs. 3.15; p-value: 0.002). Men appeared slightly more enthused about the development of technological skills and women about the honing of patient-centred care skills. Finally, interprofessional collaboration was seen as essential not only for the seamless clinical integration of AI but also for supporting patient benefit. CONCLUSION: While AI implementation advances, AI education needs to keep at pace to ensure acceptability, trust, and safe use of this technology by healthcare professionals, minimising their concerns around professional role changes and enabling them to see the opportunities of service transformation. CRITICAL RELEVANCE STATEMENT: This paper aims to map out the perceived impact of AI on the professional identity and careers of European radiographers. KEY POINTS: AI is impacting radiographers' clinical practice and changing their professional identity. Despite increasing AI awareness, AI education is still lacking across Europe. AI education is key for AI acceptability and trust by radiographers, which facilitates AI implementation and service transformation.
14A review of the ergonomic problems of agricultural activities in connection with mechanization: Some Iranian farm works as instances.PubMed
Abdollah Hayati, Afshin Marzban
Work. 2025 Nov;82(3):715-726. doi: 10.1177/10519815251351602. Epub 2025 Jul 4.
BackgroundAgricultural mechanization has transformed global farming productivity, yet many developing nations like Iran continue to face significant ergonomic challenges. Traditional farming methods persist in key sectors (date palm, saffron, dairy, vegetables, and sesame), exposing workers to hazardous conditions. These issues are compounded by uneven mechanization adoption and socioeconomic barriers in rural communities.ObjectiveThis study aims to review ergonomic risks in Iran's labor-intensive agricultural operations, analyze the effectiveness of current mechanization solutions, and identify practical interventions to improve worker health without compromising productivity. The study focuses on five mentioned economically critical sectors.MethodsWe conducted a review of 27 peer-reviewed studies (2005-2024) from Iranian and international databases. Inclusion criteria prioritized field-based ergonomic assessments, mechanization trials, and intervention studies in English/Persian. Data were synthesized to compare risks across operations and evaluate solution efficacy.ResultsThree key patterns emerged: (1) Mechanization improved work speed (e.g., 0.96 ha/labor-hour for plowing vs. 0.002 ha/labor-hour manual harvesting) but often introduced new risks (e.g., static postures in milking parlors); (2) Simple interventions showed disproportionate benefits - modified climbing tools reduced fall risks in date orchards, while kneepads decreased musculoskeletal pain in saffron harvesting; (3) Crop-specific biological traits (e.g., saffron's ground clearance) limited mechanization more than socioeconomic factors alone.ConclusionsWe propose a three-tiered approach: (1) Immediate adoption of affordable ergonomic tools (harnesses, trolleys) in high-risk sectors; (2) Redesign of machinery to address crop-specific constraints; (3) Policy reforms to subsidize small-scale solutions. This balanced strategy could reduce injuries while maintaining Iran's agricultural output.
15"Machine replacement" or "job creation": How does artificial intelligence impact employment patterns in China's manufacturing industry?PubMed
Qingqing Huo, Jing Ruan, Yan Cui
Front Artif Intell. 2024 Mar 6;7:1337264. doi: 10.3389/frai.2024.1337264. eCollection 2024.
Artificial intelligence (AI), as an important engine for promoting high-quality economic development, should not be overlooked in terms of its impact on the employment of the labor force while promoting the digital and intelligent transformation of industries. In the face of the complex international environment and non-systemic shocks, it is of great significance to explore whether it is "machine replacement" or "job creation" in the process of the integration of AI and industry, as well as the impact of technological progress on the employment pattern of the labor force, in order to promote the economic development, respond to and solve the employment problem. It is of great significance to promote economic development and cope with and solve the employment problem. Based on the task model, this paper analyses the mechanism of the impact of AI on the employment pattern of manufacturing industry. Meanwhile, based on the provincial panel data of China's manufacturing industry from 2011 to 2020, it empirically examines the impact of AI on the total employment, employment structure and employment quality of the labor force, and analyses the multiple responses of AI on the employment pattern of the manufacturing industry. The study shows that: Firstly, the level of development of AI and the total amount of employment is a positive U-shaped relationship, the short term is dominated by the substitution effect, and the long term is dominated by the creation effect; Secondly, with regard to the employment structure, low-skilled labor is more likely to be replaced. The financial, accommodation and catering industries are relatively less affected by the spillover effects of the manufacturing industry; Third, with regard to the employment quality, the gap between urban and rural incomes has eased, with per capita net income of rural residents rising to a higher degree than per capita disposable income of urban residents. Thus, in order to further address the impact of AI on the employment patterns of the labor force, the level of AI development should be increased while expanding employment channels, paying attention to labor force skills training, reinforcing the leading role of developed regions, and accelerating regional integration and urban-rural integration, so as to share the dividends of technological progress.
16Will advancement in technologies bring fear and damage human employment? Evidence from China's manufacturing industry.PubMed
Peng Wang, Donghai Li, Yangzi Wang, et al.
PLoS One. 2024 Apr 26;19(4):e0295942. doi: 10.1371/journal.pone.0295942. eCollection 2024.
Advancement in technologies such as robotic industries and artificial intelligence bring fear among human being that jobs will be substituted by robots. Base on the panel data of 28 China's manufacturing industries, this research analyzed the impact of technical progress bias on employment. First, we calculate the technical progress bias index of 28 industries base on the stochastic frontier model with transcendental logarithm function found 16 industries were toward the skilled labor while the remaining 12 industries were toward the unskilled labor. Second, the empirical results show that technical progress bias has a positive impact on the total manufacturing employment and significant positive effect on the unskilled labor, while no significant impact on skilled labor employment. Third, the threshold effect test proves that if taking industry value-added per capita or R&D capital stock as threshold variable, the threshold about the impact exist, making the impact on skilled labor was insignificant.
17Effects of Job Crafting and Leisure Crafting on Nurses' Burnout: A Machine Learning-Based Prediction Analysis.PubMed
Yu-Fang Guo, Si-Jia Wang, Virginia Plummer, et al.
J Nurs Manag. 2024 Jun 20;2024:9428519. doi: 10.1155/2024/9428519. eCollection 2024.
AIM: To explore the status of job crafting, leisure crafting, and burnout among nurses and to examine the impact of job crafting and leisure crafting variations on burnout using machine learning-based models. BACKGROUND: The prevalence of burnout among nurses poses a severe risk to their job performance, quality of healthcare, and the cohesiveness of nurse teams. Numerous studies have explored factors influencing nurse burnout; however, few involved job crafting and leisure crafting synchronously and elucidated the effect differences of the two crafting behaviors on nurse burnout. METHODS: Multicentre cross-sectional survey study. Nurses ( = 1235) from four Chinese tertiary hospitals were included. The Maslach Burnout Inventory-General Survey, the Job Crafting Scale, and the Leisure Crafting Scale were employed for data collection. Four machine learning algorithms (logistic regression model, support vector machine, random forest, and gradient boosting tree) were used to analyze the data. RESULTS: Nurses experienced mild to moderate levels of burnout and moderate to high levels of job crafting and leisure crafting. The AUC (in full) for the four models was from 0.809 to 0.821, among which the gradient boosting tree performed best, with 0.821 AUC, 0.739 accuracy, 0.470 sensitivity, 0.919 specificity, and 0.161 Brier. All models showed that job crafting was the most important predictor for burnout, while leisure crafting was identified as the second important predictor for burnout in the random forest model and gradient boosting tree model. CONCLUSION: Even if nurses experienced mild to moderate burnout, nurse managers should develop efficient interventions to reduce nurse burnout. Job crafting and leisure crafting may be beneficial preventative strategies against burnout among nurses at present. . Job and leisure crafting were identified as effective methods to reduce nurse burnout. Nurse managers should provide more opportunities for nurses' job crafting and encourage nurses crafting at their leisure time.
18Artificial Intelligence in Nursing: Technological Benefits to Nurse's Mental Health and Patient Care Quality.PubMed
Hamad Ghaleb Dailah, Mahdi Koriri, Alhussean Sabei, et al.
Healthcare (Basel). 2024 Dec 18;12(24):2555. doi: 10.3390/healthcare12242555.
Nurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare.
19AI-Assisted Tailored Intervention for Nurse Burnout: A Three-Group Randomized Controlled Trial.PubMed
Gumhee Baek, Chiyoung Cha
Worldviews Evid Based Nurs. 2025 Feb;22(1):e70003. doi: 10.1111/wvn.70003.
BACKGROUND: High-stress environments, heavy workloads, and the emotional demands of patient care, which are common challenges faced by nurses, are factors that can lead to burnout. Shift work can make traditional burnout interventions costly and difficult to implement. Artificial intelligence (AI) could offer solutions that are less constrained by time, resources, and labor. AIM: To investigate the effectiveness of an AI-assisted intervention in reducing nurse burnout. METHODS: A single-blind, three-group, randomized controlled trial of 120 nurses (40 per group) was conducted from June 2023 to July 2023. The AI-assisted tailored intervention included two 2-week programs, delivering one of four programs to the intervention group: mindfulness meditation, acceptance commitment therapy, storytelling and reflective writing, or laughter therapy. The experimental group received tailored programs based on demographic and work-related characteristics, job stress, stress response, coping strategy, and burnout dimensions (client-related, personal, and work-related). Control Group 1 self-selected their programs, while Control Group 2 was provided with online information on burnout reduction. Primary outcomes, client-related, personal, and work-related burnout, were measured at baseline, week 2, and week 4. Secondary outcomes, job stress, stress responses, and coping strategies, were assessed at baseline and week 4. Data were analyzed using ANOVA, repeated measures ANOVA, and the Scheffé test for post hoc analysis. RESULTS: The experimental group showed significant reductions in client-related burnout (F = 7.725, p = 0.001) and personal burnout (F = 10.967, p < 0.0001) compared to the other groups. Significant effects of time and time × group interactions were observed for client-related and personal burnout, with time effects noted for work-related burnout. Stress response reduction was highest in Control Group 1, followed by the experimental group and Control Group 2 (F = 3.07, p = 0.017). LINKING EVIDENCE TO ACTION: AI algorithms could provide tailored programs to mitigate nurse burnout, particularly in client-related and personal burnout. Reducing nurse burnout could contribute to the quality of care. TRIAL REGISTRATION: This trial is registered with the Clinical Research Information Service (KCT0008546).
20Development of an Artificial Intelligence-Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial.PubMed
Aram Cho, Chiyoung Cha, Gumhee Baek
J Med Internet Res. 2024 Jun 21;26:e54029. doi: 10.2196/54029.
BACKGROUND: Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were general and did not consider specific burnout dimensions and individual characteristics. OBJECTIVE: The objectives of this study were to develop and optimize the first tailored mobile intervention for nurse burnout, which recommends programs based on artificial intelligence (AI) algorithms, and to test its usability, effectiveness, and satisfaction. METHODS: In this study, an AI-based mobile intervention, Nurse Healing Space, was developed to provide tailored programs for nurse burnout. The 4-week program included mindfulness meditation, laughter therapy, storytelling, reflective writing, and acceptance and commitment therapy. The AI algorithm recommended one of these programs to participants by calculating similarity through a pretest consisting of participants' demographics, research variables, and burnout dimension scores measured with the Copenhagen Burnout Inventory. After completing a 4-week program, burnout, job stress, stress response using the Stress Response Inventory Modified Form, the usability of the app, coping strategy by the coping strategy indicator, and program satisfaction (1: very dissatisfied; 5: very satisfied) were measured. The AI recognized the recommended program as effective if the user's burnout score reduced after the 2-week program and updated the algorithm accordingly. After a pilot test (n=10), AI optimization was performed (n=300). A paired 2-tailed t test, ANOVA, and the Spearman correlation were used to test the effect of the intervention and algorithm optimization. RESULTS: Nurse Healing Space was implemented as a mobile app equipped with a system that recommended 1 program out of 4 based on similarity between users through AI. The AI algorithm worked well in matching the program recommended to participants who were most similar using valid data. Users were satisfied with the convenience and visual quality but were dissatisfied with the absence of notifications and inability to customize the program. The overall usability score of the app was 3.4 out of 5 points. Nurses' burnout scores decreased significantly after the completion of the first 2-week program (t=7.012; P<.001) and reduced further after the second 2-week program (t=2.811; P=.01). After completing the Nurse Healing Space program, job stress (t=6.765; P<.001) and stress responses (t=5.864; P<.001) decreased significantly. During the second 2-week program, the burnout level reduced in the order of participation (r=-0.138; P=.04). User satisfaction increased for both the first (F=3.493; P=.03) and second programs (F=3.911; P=.02). CONCLUSIONS: This program effectively reduced burnout, job stress, and stress responses. Nurse managers were able to prevent nurses from resigning and maintain the quality of medical services using this AI-based program to provide tailored interventions for nurse burnout. Thus, this app could improve qualitative health care, increase employee satisfaction, reduce costs, and ultimately improve the efficiency of the health care system.
21The Impact of COVID-19 on Nursing Practice: Lessons Learned and Future Trends.PubMed
Sirwan K Ahmed
Cureus. 2023 Dec 7;15(12):e50098. doi: 10.7759/cureus.50098. eCollection 2023 Dec.
The coronavirus disease 2019 (COVID-19) pandemic significantly impacted healthcare givers, especially nurses, who were at the forefront of the crisis. It highlighted the importance of preparedness, adaptability, and technology in healthcare systems. Nurses faced challenges like personal protective equipment (PPE) shortages, high patient numbers, and evolving protocols, emphasizing the need for robust plans and flexible infrastructures. Telehealth became crucial, ensuring care continuity and access, particularly for underserved groups. Mental health support for nurses became vital due to extreme stress and pressure. The pandemic also emphasized ongoing education and upskilling in nursing and highlighted the role of AI in healthcare solutions. Interdisciplinary collaboration among healthcare professionals gained importance, shaping a more holistic approach to patient care. This editorial highlights the impact of the COVID-19 pandemic on the nursing profession, highlighting the crucial significance of being well-prepared, adaptable, technologically advanced, and collaborative in order to ensure a more robust healthcare system in the future.
22Opportunities and Challenges for Digital Health and Artificial Intelligence to Support Nurses: Results of a Survey of Nursing Informaticists.PubMed
Meghan Reading Turchioe, Robin Austin, Kay Lytle
Comput Inform Nurs. 2025 Jul 1;43(7):e01279. doi: 10.1097/CIN.0000000000001279.
Artificial intelligence and other digital health technologies may optimize nurses' work. Therefore, we aimed to examine the roles of nurses in facilitating the adoption of digital health technologies and identify opportunities for these technologies to reduce burnout. We conducted a cross-sectional survey study focused on nurses' use of digital health and artificial intelligence technology with nursing informaticists. Data collection was guided by the implementation science framework, Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability. Participants were recruited electronically through professional nursing informatics organizations. Survey data were analyzed using basic descriptive statistics. Fifty-two participants from across the United States completed the survey. Telehealth (73%), patient portals (71%), and medical-grade devices (69%) were most frequently used, whereas artificial intelligence was frequently used by only 38%. Staffing shortages (88%), low staff retention (81%), and inadequate support when adopting new technologies (52%) were among the key drivers of nursing burnout. Participants endorsed most nursing tasks as being supported by digital health, especially patient assessment and evaluating outcomes, and especially artificial intelligence. Engaging nurses early in the process of developing and deploying digital health, especially artificial intelligence, may help address burnout by producing more nursing-centered technologies and providing technology-enabled nursing work alternatives to bedside care.
23Artificial intelligence in medical education - perception among medical students.PubMed
Preetha Jackson, Gayathri Ponath Sukumaran, Chikku Babu, et al.
BMC Med Educ. 2024 Jul 27;24(1):804. doi: 10.1186/s12909-024-05760-0.
BACKGROUND: As Artificial Intelligence (AI) becomes pervasive in healthcare, including applications like robotic surgery and image analysis, the World Medical Association emphasises integrating AI education into medical curricula. This study evaluates medical students' perceptions of 'AI in medicine', their preferences for AI training in education, and their grasp of AI's ethical implications in healthcare. MATERIALS & METHODS: A cross-sectional study was conducted among 325 medical students in Kerala using a pre-validated, semi structured questionnaire. The survey collected demographic data, any past educational experience about AI, participants' self-evaluation of their knowledge and evaluated self-perceived understanding of applications of AI in medicine. Participants responded to twelve Likert-scale questions targeting perceptions and ethical aspects and their opinions on suggested topics on AI to be included in their curriculum. RESULTS & DISCUSSION: AI was viewed as an assistive technology for reducing medical errors by 57.2% students and 54.2% believed AI could enhance medical decision accuracy. About 49% agreed that AI could potentially improve accessibility to healthcare. Concerns about AI replacing physicians were reported by 37.6% and 69.2% feared a reduction in the humanistic aspect of medicine. Students were worried about challenges to trust (52.9%), patient-physician relationships (54.5%) and breach of professional confidentiality (53.5%). Only 3.7% felttotally competent in informing patients about features and risks associated with AI applications. Strong demand for structured AI training was expressed, particularly on reducing medical errors (76.9%) and ethical issues (79.4%). CONCLUSION: This study highlights medical students' demand for structured AI training in undergraduate curricula, emphasising its importance in addressing evolving healthcare needs and ethical considerations. Despite widespread ethical concerns, the majority perceive AI as an assistive technology in healthcare. These findings provide valuable insights for curriculum development and defining learning outcomes in AI education for medical students.
24Medical students' attitude towards artificial intelligence: a multicentre survey.PubMed
D Pinto Dos Santos, D Giese, S Brodehl, et al.
Eur Radiol. 2019 Apr;29(4):1640-1646. doi: 10.1007/s00330-018-5601-1. Epub 2018 Jul 6.
OBJECTIVES: To assess undergraduate medical students' attitudes towards artificial intelligence (AI) in radiology and medicine. MATERIALS AND METHODS: A web-based questionnaire was designed using SurveyMonkey, and was sent out to students at three major medical schools. It consisted of various sections aiming to evaluate the students' prior knowledge of AI in radiology and beyond, as well as their attitude towards AI in radiology specifically and in medicine in general. Respondents' anonymity was ensured. RESULTS: A total of 263 students (166 female, 94 male, median age 23 years) responded to the questionnaire. Around 52% were aware of the ongoing discussion about AI in radiology and 68% stated that they were unaware of the technologies involved. Respondents agreed that AI could potentially detect pathologies in radiological examinations (83%) but felt that AI would not be able to establish a definite diagnosis (56%). The majority agreed that AI will revolutionise and improve radiology (77% and 86%), while disagreeing with statements that human radiologists will be replaced (83%). Over two-thirds agreed on the need for AI to be included in medical training (71%). In sub-group analyses male and tech-savvy respondents were more confident on the benefits of AI and less fearful of these technologies. CONCLUSION: Contrary to anecdotes published in the media, undergraduate medical students do not worry that AI will replace human radiologists, and are aware of the potential applications and implications of AI on radiology and medicine. Radiology should take the lead in educating students about these emerging technologies. KEY POINTS: • Medical students are aware of the potential applications and implications of AI in radiology and medicine in general. • Medical students do not worry that the human radiologist or physician will be replaced. • Artificial intelligence should be included in medical training.
25Perceptions and Attitudes of Registered Nurses and Nursing Students Toward Advanced Technology and Artificial Intelligence: A Review of Literature.PubMed
Omar Abdelaziz, Sohye Lee, Sheri Howard, et al.
Comput Inform Nurs. 2025 Mar 1;43(3):e01221. doi: 10.1097/CIN.0000000000001221.
The use of technology in healthcare and healthcare education settings has increased rapidly across the United States and accelerated due to the COVID-19 pandemic. However, perceptions of new technologies in clinical nursing and nursing education are not well understood. Yet, understanding perceptions of registered nurses and nursing students toward advanced technology and artificial intelligence in clinical care and education is crucial if we are to implement these care delivery and educational innovations. This literature review investigates existing literature on registered nurses' and nursing students' attitudes toward advanced technology and artificial intelligence in nursing, including nursing education. Ten peer-reviewed studies published between 2017 and 2022 were reviewed. Findings revealed positive perceptions, such as improved patient care, efficiency, and reduced human error, but also concerns about job displacement, loss of human touch, and ethical/legal issues. Challenges in implementation, adequate training in technologies, and how technologies may reduce the human connection aspect of nursing care were identified. By recognizing the attitudes and perceptions of registered nurses and nursing students toward these advanced technologies, we can better ensure that it is ethically, effectively, and responsibly integrated into nursing practice and education.
26Medical Student Experiences and Perceptions of ChatGPT and Artificial Intelligence: Cross-Sectional Study.PubMed
Saif M I Alkhaaldi, Carl H Kassab, Zakia Dimassi, et al.
JMIR Med Educ. 2023 Dec 22;9:e51302. doi: 10.2196/51302.
BACKGROUND: Artificial intelligence (AI) has the potential to revolutionize the way medicine is learned, taught, and practiced, and medical education must prepare learners for these inevitable changes. Academic medicine has, however, been slow to embrace recent AI advances. Since its launch in November 2022, ChatGPT has emerged as a fast and user-friendly large language model that can assist health care professionals, medical educators, students, trainees, and patients. While many studies focus on the technology's capabilities, potential, and risks, there is a gap in studying the perspective of end users. OBJECTIVE: The aim of this study was to gauge the experiences and perspectives of graduating medical students on ChatGPT and AI in their training and future careers. METHODS: A cross-sectional web-based survey of recently graduated medical students was conducted in an international academic medical center between May 5, 2023, and June 13, 2023. Descriptive statistics were used to tabulate variable frequencies. RESULTS: Of 325 applicants to the residency programs, 265 completed the survey (an 81.5% response rate). The vast majority of respondents denied using ChatGPT in medical school, with 20.4% (n=54) using it to help complete written assessments and only 9.4% using the technology in their clinical work (n=25). More students planned to use it during residency, primarily for exploring new medical topics and research (n=168, 63.4%) and exam preparation (n=151, 57%). Male students were significantly more likely to believe that AI will improve diagnostic accuracy (n=47, 51.7% vs n=69, 39.7%; P=.001), reduce medical error (n=53, 58.2% vs n=71, 40.8%; P=.002), and improve patient care (n=60, 65.9% vs n=95, 54.6%; P=.007). Previous experience with AI was significantly associated with positive AI perception in terms of improving patient care, decreasing medical errors and misdiagnoses, and increasing the accuracy of diagnoses (P=.001, P<.001, P=.008, respectively). CONCLUSIONS: The surveyed medical students had minimal formal and informal experience with AI tools and limited perceptions of the potential uses of AI in health care but had overall positive views of ChatGPT and AI and were optimistic about the future of AI in medical education and health care. Structured curricula and formal policies and guidelines are needed to adequately prepare medical learners for the forthcoming integration of AI in medicine.
27Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial.PubMed
Emilia Brügge, Sarah Ricchizzi, Malin Arenbeck, et al.
BMC Med Educ. 2024 Nov 28;24(1):1391. doi: 10.1186/s12909-024-06399-7.
BACKGROUND: Clinical decision-making (CDM) refers to physicians' ability to gather, evaluate, and interpret relevant diagnostic information. An integral component of CDM is the medical history conversation, traditionally practiced on real or simulated patients. In this study, we explored the potential of using Large Language Models (LLM) to simulate patient-doctor interactions and provide structured feedback. METHODS: We developed AI prompts to simulate patients with different symptoms, engaging in realistic medical history conversations. In our double-blind randomized design, the control group participated in simulated medical history conversations with AI patients (control group), while the intervention group, in addition to simulated conversations, also received AI-generated feedback on their performances (feedback group). We examined the influence of feedback based on their CDM performance, which was evaluated by two raters (ICC = 0.924) using the Clinical Reasoning Indicator - History Taking Inventory (CRI-HTI). The data was analyzed using an ANOVA for repeated measures. RESULTS: Our final sample included 21 medical students (age = 22.10 years, semester = 4, 14 females). At baseline, the feedback group (mean = 3.28 ± 0.09 [standard deviation]) and the control group (3.21 ± 0.08) achieved similar CRI-HTI scores, indicating successful randomization. After only four training sessions, the feedback group (3.60 ± 0.13) outperformed the control group (3.02 ± 0.12), F (1,18) = 4.44, p = .049 with a strong effect size, partial η = 0.198. Specifically, the feedback group showed improvements in the subdomains of CDM of creating context (p = .046) and securing information (p = .018), while their ability to focus questions did not improve significantly (p = .265). CONCLUSION: The results suggest that AI-simulated medical history conversations can support CDM training, especially when combined with structured feedback. Such training format may serve as a cost-effective supplement to existing training methods, better preparing students for real medical history conversations.
28Embracing ChatGPT for Medical Education: Exploring Its Impact on Doctors and Medical Students.PubMed
Yijun Wu, Yue Zheng, Baijie Feng, et al.
JMIR Med Educ. 2024 Apr 10;10:e52483. doi: 10.2196/52483.
ChatGPT (OpenAI), a cutting-edge natural language processing model, holds immense promise for revolutionizing medical education. With its remarkable performance in language-related tasks, ChatGPT offers personalized and efficient learning experiences for medical students and doctors. Through training, it enhances clinical reasoning and decision-making skills, leading to improved case analysis and diagnosis. The model facilitates simulated dialogues, intelligent tutoring, and automated question-answering, enabling the practical application of medical knowledge. However, integrating ChatGPT into medical education raises ethical and legal concerns. Safeguarding patient data and adhering to data protection regulations are critical. Transparent communication with students, physicians, and patients is essential to ensure their understanding of the technology's purpose and implications, as well as the potential risks and benefits. Maintaining a balance between personalized learning and face-to-face interactions is crucial to avoid hindering critical thinking and communication skills. Despite challenges, ChatGPT offers transformative opportunities. Integrating it with problem-based learning, team-based learning, and case-based learning methodologies can further enhance medical education. With proper regulation and supervision, ChatGPT can contribute to a well-rounded learning environment, nurturing skilled and knowledgeable medical professionals ready to tackle health care challenges. By emphasizing ethical considerations and human-centric approaches, ChatGPT's potential can be fully harnessed in medical education, benefiting both students and patients alike.
29Medical students' attitudes toward AI in education: perception, effectiveness, and its credibility.PubMed
Abdul Sami, Fateema Tanveer, Khadeejah Sajwani, et al.
BMC Med Educ. 2025 Jan 17;25(1):82. doi: 10.1186/s12909-025-06704-y.
BACKGROUND: The rapid advancement of artificial intelligence (AI) has revolutionized both medical education and healthcare by delivering innovative tools that enhance learning and improve overall outcomes. The study aimed to assess students' perceptions regarding the credibility and effectiveness of AI as a learning tool and to explore the dynamics of integrating AI in medical education. METHODOLOGY: A cross-sectional study was carried out across medical colleges in Pakistan. A 26-question survey was developed using Google Forms from previously validated studies. The survey assessed demographics of participants, basic understanding of AI, AI as a learning tool in medical education and socio-ethical impacts of the use of AI. The data was analyzed using SPSS (v 26.0) to derive descriptive and inferential statistics. RESULT: A total of 702 medical students aged 18 to 26 years (mean age 20.50 ± 1.6 years) participated in the study. The findings revealed a generally favorable attitude towards AI among medical students (80.3%), with the majority considering it an effective (60.8%) and credible (58.4%) learning tool in medical education. Students agreed that AI learning optimized their study time (60.3%) and provided up-to-date medical information (63.1%). Notably, 65.7% of students found AI more efficient in helping them grasp medical concepts compared to traditional tools like books and lectures, while 66.8% reported receiving more accurate answers to their medical inquiries through AI. The study highlighted that medical students view traditional tools as becoming increasingly outdated (59%), emphasizing the importance of integrating AI into medical education and creating dedicated AI tools (80%) for the medical education. CONCLUSION: This study demonstrated that AI is an effective and credible tool in medical education, offering personalized learning experiences and improved educational outcomes. AI tools are helping students learn medical concepts by cutting down on study-time, providing accurate answers, and ultimately improving study outcomes. We recommend developing dedicated AI tools for medical education and their formal integration into medical curricula, along with appropriate regulatory oversight to ensure AI can enhance human abilities rather than acting as a replacement for humans.
30Knowledge, Attitudes, Practices, and Barriers Regarding the Integration of Artificial Intelligence in Nursing and Health Sciences Education: A Systematic Review.PubMed
Nesreen Alqaissi, Mohammed Qtait
SAGE Open Nurs. 2025 Sep 2;11:23779608251374185. doi: 10.1177/23779608251374185. eCollection 2025 Jan-Dec.
BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare education and practice, making it essential for nursing and health sciences students to develop relevant competencies. However, their preparedness to engage meaningfully with AI in academic and clinical environments remains uncertain. OBJECTIVES: This systematic review synthesizes global evidence on the knowledge, attitudes, practices, and barriers (KAPB) related to AI among students in nursing, medicine, pharmacy, and allied health disciplines. METHODS: Following PRISMA 2020 guidelines, a systematic search was conducted in PubMed, Scopus, CINAHL, and Google Scholar for peer-reviewed articles published between January 2020 and February 2025. Fourteen studies meeting inclusion criteria were analyzed using narrative synthesis. Both quantitative and qualitative studies were included. The review protocol was not registered. RESULTS: Students generally demonstrated high awareness of AI, but formal training was limited. Knowledge was often acquired informally through social media or peer networks. While attitudes toward AI were largely positive, students expressed ethical concerns and anxiety related to AI use. Practical engagement with AI was mostly restricted to academic writing tasks, with minimal clinical application. Major barriers included the absence of AI-focused curricula, limited faculty expertise, inadequate infrastructure, and concerns over data privacy, ethics, and job displacement. CONCLUSION: Despite growing interest in AI, significant gaps remain in health sciences education. Comprehensive strategies such as curriculum integration, faculty development, and ethical training are urgently needed to foster responsible, confident, and clinically relevant AI adoption among future healthcare professionals.
31Medical students' perceptions of the impact of artificial intelligence in radiology.PubMed
G Caparrós Galán, F Sendra Portero
Radiologia (Engl Ed). 2022 Nov-Dec;64(6):516-524. doi: 10.1016/j.rxeng.2021.03.008.
OBJECTIVES: To analyse medical students' perceptions of the impact of artificial intelligence in radiology. MATERIAL AND METHODS: A structured questionnaire comprising 28 items organised into six sections was distributed to students of medicine in Spain in December 2019. RESULTS: A total of 341 students responded. Of these, 27 (7.9%) included radiology among their three main choices for specialization, and 51.9% considered that they clearly understood what artificial intelligence is. The overall rate of correct answers to the objective true-or-false questions about artificial intelligence was 70.7%. Whereas 75.9% expressed their disagreement with the hypothesis that artificial intelligence would replace radiologists, only 41.9% disagreed with the hypothesis that the demand for radiologists would decrease in the future. Only 36.7% expressed concerns about the role of artificial intelligence related to choosing radiology as a specialty. A greater proportion of students in the early years of medical school agreed with statements that radiologists accept artificial-intelligence-related technological changes and work with the industry to apply them as well as with statements about the need to include basic training about artificial intelligence in the medical school curriculum. CONCLUSIONS: The students surveyed are aware of the impact of artificial intelligence in daily life, but not of the current debate about its potential applications in radiology. In general, they think that artificial intelligence will revolutionise radiology without having an alarming effect on the employability of radiologists. The students surveyed think that it is necessary to provide basic training about artificial intelligence in undergraduate medical school programs.
32Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study.PubMed
Ahmed Hafez Allam, Nael Kamel Eltewacy, Yasmeen Jamal Alabdallat, et al.
Eur Radiol. 2024 Jul;34(7):1-14. doi: 10.1007/s00330-023-10509-2. Epub 2023 Dec 27.
OBJECTIVES: We aimed to assess undergraduate medical students' knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine. METHODS: A multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses. RESULTS: Of the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies. CONCLUSIONS: Arab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum. CLINICAL RELEVANCE STATEMENT: This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula. KEY POINTS: • Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology. • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea. • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.
33Medical students' perceptions of an artificial intelligence (AI) assisted diagnosing program.PubMed
Emely Robleto, Ali Habashi, Mary-Ann Benites Kaplan, et al.
Med Teach. 2024 Sep;46(9):1180-1186. doi: 10.1080/0142159X.2024.2305369. Epub 2024 Feb 2.
As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.
34GPT-4 Artificial Intelligence Model Outperforms ChatGPT, Medical Students, and Neurosurgery Residents on Neurosurgery Written Board-Like Questions.PubMed
Gage A Guerra, Hayden Hofmann, Sina Sobhani, et al.
World Neurosurg. 2023 Nov;179:e160-e165. doi: 10.1016/j.wneu.2023.08.042. Epub 2023 Aug 18.
BACKGROUND: Artificial intelligence (AI) and machine learning have transformed health care with applications in various specialized fields. Neurosurgery can benefit from artificial intelligence in surgical planning, predicting patient outcomes, and analyzing neuroimaging data. GPT-4, an updated language model with additional training parameters, has exhibited exceptional performance on standardized exams. This study examines GPT-4's competence on neurosurgical board-style questions, comparing its performance with medical students and residents, to explore its potential in medical education and clinical decision-making. METHODS: GPT-4's performance was examined on 643 Congress of Neurological Surgeons Self-Assessment Neurosurgery Exam (SANS) board-style questions from various neurosurgery subspecialties. Of these, 477 were text-based and 166 contained images. GPT-4 refused to answer 52 questions that contained no text. The remaining 591 questions were inputted into GPT-4, and its performance was evaluated based on first-time responses. Raw scores were analyzed across subspecialties and question types, and then compared to previous findings on Chat Generative pre-trained transformer performance against SANS users, medical students, and neurosurgery residents. RESULTS: GPT-4 attempted 91.9% of Congress of Neurological Surgeons SANS questions and achieved 76.6% accuracy. The model's accuracy increased to 79.0% for text-only questions. GPT-4 outperformed Chat Generative pre-trained transformer (P < 0.001) and scored highest in pain/peripheral nerve (84%) and lowest in spine (73%) categories. It exceeded the performance of medical students (26.3%), neurosurgery residents (61.5%), and the national average of SANS users (69.3%) across all categories. CONCLUSIONS: GPT-4 significantly outperformed medical students, neurosurgery residents, and the national average of SANS users. The mode's accuracy suggests potential applications in educational settings and clinical decision-making, enhancing provider efficiency, and improving patient care.
35Nursing students' readiness and anxiety regarding medical artificial intelligence: A mixed-methods study.PubMed
Rukiye Burucu, Hilal Türkben Polat
Nurse Educ Pract. 2025 Oct;88:104538. doi: 10.1016/j.nepr.2025.104538. Epub 2025 Sep 9.
AIM: The aim of this study is to examine the preparedness levels of nursing students for Medical Artificial Intelligence and Artificial Intelligence Anxiety. DESIGN: This is a descriptive mixed-method study. METHODS: The medical artificial intelligence readiness scale, artificial intelligence anxiety scale and semi-structured interview form were applied to the nursing department students of two separate faculties (n:358). The focus group interview was conducted with 19 participants. Quantitative and qualitative data were analyzed separately and combined. RESULTS: The average artificial intelligence awareness score of all the participants participating in the study was 75.05 ± 11,805, and the average artificial intelligence anxiety score was 49.59 ± 11,263. The average artificial intelligence awareness score of the participants in the qualitative part of the study was 72.89 ± 10.519 and the average artificial intelligence anxiety score was 70.68 ± 5.165. 145 opinions were identified regarding the concerns of individuals with artificial intelligence anxiety. These opinions are grouped under four sub-themes; Two sub-themes were evaluated as "complementary" and the other two as "convergent" with quantitative data. Sequential explanatory design was used in the study. After the quantitative data were evaluated, qualitative data were collected, evaluated and the results were integrated. CONCLUSION: It was determined that the students were ready for artificial intelligence; they believed that artificial intelligence increases productivity and acts as a facilitator; but they also had various concerns about artificial intelligence. Qualitative and quantitative data supported each other. PATIENT OR PUBLIC CONTRIBUTION: Increasing the awareness of nursing students and nurses on artificial intelligence can contribute to public health. CLINICAL TRIAL REGISTRATION: NCT06623448.
36The Effect of AI Education on Nursing Student AI Literacy and Anxiety.PubMed
Albany LaGore, Lindsay Colby, Kristina Sinsabaugh, et al.
J Nurs Educ. 2025 Sep;64(9):587-590. doi: 10.3928/01484834-20250415-04. Epub 2025 Sep 1.
BACKGROUND: The growing use of artificial intelligence (AI) in health care raises concern, as nursing students are not adequately educated on its use in practice. This study evaluated the effect of an AI education module on undergraduate nursing students' AI literacy and anxiety. METHOD: Students completed surveys measuring AI literacy and anxiety before and after the module, which included videos, articles, a presentation, and an assignment using ChatGPT. RESULTS: Paired-samples t test showed a significant increase in AI literacy from pretest ( = 4.90 [ = .95]) to posttest ( = 5.72 [ = .92]), (111) = -9.49, < .001 and a significant decrease in AI anxiety from pre-test ( = 4.45 [ = 1.27]) to posttest ( = 3.58 [ = 1.32]), (111) = 7.76, < .001. CONCLUSION: The AI education module is an effective teaching tool to increase nursing students' AI literacy and decrease anxiety.
37Generative AI Backstories for Simulation Preparation.PubMed
Janet M Reed, Tracy M Dodson
Nurse Educ. 2024;49(4):184-188. doi: 10.1097/NNE.0000000000001590. Epub 2023 Dec 25.
BACKGROUND: Developing engaging presimulation learning materials that provide contextualized patient information is needed to best prepare students for nursing simulation. One emerging strategy that can be used by educators to create visual images for storytelling is generative artificial intelligence (AI). PURPOSE: The purpose of this pilot study was to determine how the use of generative AI-created patient backstories as a presimulation strategy might affect student engagement and learning in nursing simulation. METHODS: A qualitative cross-sectional survey with content analysis was completed with undergraduate nursing students following an acute care simulation. RESULTS: Student surveys point to positive pedagogical outcomes of using AI image generation as a strategy to prepare for simulation such as decreased anxiety in simulation, increased preparatory knowledge, and increased emotional connection with the patient's story. CONCLUSIONS: Images created with generative AI hold promise for future research and transforming nursing education.
38Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety.PubMed
Zeliha Demir-Kaymak, Zekiye Turan, Nazli Unlu-Bidik, et al.
Nurse Educ Pract. 2024 Jul;78:103994. doi: 10.1016/j.nepr.2024.103994. Epub 2024 May 24.
BACKGROUND: Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed. AIM: This study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students. METHODS: This study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used. RESULTS: No significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety. CONCLUSION: Midwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.
39Evaluation of the effectiveness of artificial intelligence assisted interactive screen-based simulation in breast self-examination: An innovative approach in nursing students.PubMed
Sahika Simsek-Cetinkaya, Selda Karaveli Cakir
Nurse Educ Today. 2023 Aug;127:105857. doi: 10.1016/j.nedt.2023.105857. Epub 2023 May 24.
BACKGROUND: Breast self-examination is important in the early diagnosis of breast cancer. The use of traditional education methods is insufficient for student nurses to gain breast self-examination skills in nurse education. New and different education methods are needed to gain skills in nurse education. OBJECTIVE: The aim of this study was to evaluate the effectiveness of artificial intelligence-assisted screen-based simulations practice and standard patient simulation in teaching breast self-examination skills in nursing undergraduate students. DESIGN: This study was a comparative intervention trial. SETTINGS: This study was conducted at a university in XX, in XXX in the first semesters of the academic years 2022-2023. METHODS: This study enlisted 103 students enrolled in first year in a nursing department. Students were randomized into artificial intelligence-assisted screen-based simulations practice group (n = 52) and standard patient simulation group (n = 51). Data were collected using student description form, breast self-examination checklist, student satisfaction and self-confidence in learning scale, Spielberger's state and trait anxiety inventory. RESULTS: The highest score regarding the total score means of breast self-examination skills belonged to the standard patient simulation group, and the differences between the groups were found to be statistically significant (p < 0.05). Although the mean score of anxiety levels of the students' artificial intelligence-assisted screen-based simulations practice group was higher than the standard patient simulation (p < 0.05). The mean score of the students' satisfaction with the simulation was higher in artificial intelligence-assisted screen-based simulations practice group than the standard patient simulation group (p < 0.05). CONCLUSIONS: The results of the research showed that the use of artificial intelligence-assisted simulation learning increased students' satisfaction, but at the same time students' anxiety increased. In addition, artificial intelligence-assisted simulation learning is not as effective as standard patient simulation learning in gaining breast self-examination skills.
40Artificial intelligence, recessionary pressures and population health.PubMed
Jo-An Occhipinti, Ante Prodan, William Hynes, et al.
Bull World Health Organ. 2025 Feb 1;103(2):155-163. doi: 10.2471/BLT.24.291950. Epub 2025 Jan 2.
Economic and labour policies have a considerable influence on health and well-being through direct financial impacts, and by shaping social and physical environments. Strong economies are important for public health investment and employment, yet the rapid rise of generative artificial intelligence (AI) has the potential to reshape economies, presenting challenges beyond mere temporary market disruption. Generative AI can perform non-routine cognitive tasks, previously unattainable though traditional automation, creating new efficiencies. While this technology offers opportunities for innovation and productivity, its labour-displacing potential raises serious concerns about economic stability and social equity, both of which are critical to health. Job displacement driven by generative AI could worsen income inequality, shrink middle-class opportunities and reduce consumer demand, triggering recessionary pressures. In this article, we propose the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures may emerge, and which market forces alone cannot correct. Traditional responses to such pressures, like fiscal stimulus or monetary easing, may be ineffective in addressing structural disruptions to labour markets caused by generative AI. We call for a proactive global response to harness the benefits of generative AI while mitigating risks. This response should focus on reorienting economic systems towards collective well-being, as emphasized in the World Health Assembly resolution and the United Nations' Global Digital Compact. Integrated strategies that combine fiscal policy, regulation and social policies are critical to ensuring generative AI advances societal health and equity while avoiding harm from excessive job displacement.
41Human-Robot Collaborations in Smart Manufacturing Environments: Review and Outlook.PubMed
Uqba Othman, Erfu Yang
Sensors (Basel). 2023 Jun 17;23(12):5663. doi: 10.3390/s23125663.
The successful implementation of Human-Robot Collaboration (HRC) has become a prominent feature of smart manufacturing environments. Key industrial requirements, such as flexibility, efficiency, collaboration, consistency, and sustainability, present pressing HRC needs in the manufacturing sector. This paper provides a systemic review and an in-depth discussion of the key technologies currently being employed in smart manufacturing with HRC systems. The work presented here focuses on the design of HRC systems, with particular attention given to the various levels of Human-Robot Interaction (HRI) observed in the industry. The paper also examines the key technologies being implemented in smart manufacturing, including Artificial Intelligence (AI), Collaborative Robots (Cobots), Augmented Reality (AR), and Digital Twin (DT), and discusses their applications in HRC systems. The benefits and practical instances of deploying these technologies are showcased, emphasizing the substantial prospects for growth and improvement in sectors such as automotive and food. However, the paper also addresses the limitations of HRC utilization and implementation and provides some insights into how the design of these systems should be approached in future work and research. Overall, this paper provides new insights into the current state of HRC in smart manufacturing and serves as a useful resource for those interested in the ongoing development of HRC systems in the industry.
42Drivers of Automation and Consequences for Jobs in Engineering Services: An Agent-Based Modelling Approach.PubMed
Hildegunn Kyvik Nordås, Franziska Klügl
Front Robot AI. 2021 May 10;8:637125. doi: 10.3389/frobt.2021.637125. eCollection 2021.
New technology is of little use if it is not adopted, and surveys show that less than 10% of firms use Artificial Intelligence. This paper studies the uptake of AI-driven automation and its impact on employment, using a dynamic agent-based model (ABM). It simulates the adoption of automation software as well as job destruction and job creation in its wake. There are two types of agents: manufacturing firms and engineering services firms. The agents choose between two business models: consulting or automated software. From the engineering firms' point of view, the model exhibits static economies of scale in the software model and dynamic (learning by doing) economies of scale in the consultancy model. From the manufacturing firms' point of view, switching to the software model requires restructuring of production and there are network effects in switching. The ABM matches engineering and manufacturing agents and derives employment of engineers and the tasks they perform, i.e. consultancy, software development, software maintenance, or employment in manufacturing. We find that the uptake of software is gradual; slow in the first few years and then accelerates. Software is fully adopted after about 18 years in the base line run. Employment of engineers shifts from consultancy to software development and to new jobs in manufacturing. Spells of unemployment may occur if skilled jobs creation in manufacturing is slow. Finally, the model generates boom and bust cycles in the software sector.
43Selective insensitivity to income held by the richest.PubMed
Barnabas Szaszi, Hooman Habibnia, Josephine Tan, et al.
PNAS Nexus. 2024 Sep 17;3(9):pgae333. doi: 10.1093/pnasnexus/pgae333. eCollection 2024 Sep.
The misperception of income inequality is often touted as a critical barrier to more widespread support of redistributive policies. Here, we examine to what extent and why (mis)perceptions vary systematically across the income distribution. Drawing on data from four studies ( = 2,744)-including a representative sample and preregistered incentive-compatible experiments-we offer converging evidence that people specifically underestimate the amount of income held by the top of the income distribution. While this selective underestimation is likely driven by multiple mechanisms, including systemic factors, we find that cognitive biases contribute to the observed pattern of results. The rise of inequality in many developed countries has been documented before, and the fact that this growing inequality is largely driven by the outsized gains of the richest individuals may pose new challenges previously underappreciated: our theory and findings highlight that cognitive biases pose a key obstacle to people's recognition of the concentration of income among the richest individuals, and may potentially distort their preferences for redistribution. We conclude by discussing future directions for research and the importance of incorporating behavioral and cognitive limitations into the design of redistributive public policy.
44Dynamics of labor and capital in AI vs. non-AI industries: A two-industry model analysis.PubMed
Xu Huang
PLoS One. 2024 Jan 24;19(1):e0295150. doi: 10.1371/journal.pone.0295150. eCollection 2024.
There is an imbalance in the development of artificial intelligence between industries. Compared to non-AI enterprise, AI- enterprise will save labor, enhance innovation capabilities, and improve production efficiency. By constructing a two-industry model of AI and non-AI enterprise, this paper finds that with the development of artificial intelligence in the same industry, the AI enterprise will occupy a dominant position, attracting labor and capital from the non-AI enterprise into the AI enterprise. In different industries, the development of artificial intelligence improves the production efficiency of the enterprise. However, due to the price effect, non-AI enterprise benefits more. Labor and capital flow from AI enterprise to non-AI enterprise. In order to promote the improvement of production efficiency in the whole society, the government can tax non-AI enterprise and subsidize them to AI enterprise. Taxation promotes the degree of automation and the improvement of production efficiency, but it has only a short-term effect on the development of AI. At the same time, taxation inhibits the development of non-AI enterprise, and there is a high risk of unemployment. When both industries use artificial intelligence for production, the labor share and the capital share of the two industries will tend to the same value. The convergence of technology measures is conducive to increasing labor income share and reducing income inequality, but it is not conducive to innovation.
45A deep learning model identifies emphasis on hard work as an important predictor of income inequality.PubMed
Abhishek Sheetal, Srinwanti H Chaudhury, Krishna Savani
Sci Rep. 2022 Jun 14;12(1):9845. doi: 10.1038/s41598-022-13902-x.
High levels of income inequality can persist in society only if people accept the inequality as justified. To identify psychological predictors of people's tendency to justify inequality, we retrained a pre-existing deep learning model to predict the extent to which World Values Survey respondents believed that income inequality is necessary. A feature importance analysis revealed multiple items associated with the importance of hard work as top predictors. As an emphasis on hard work is a key component of the Protestant Work Ethic, we formulated the hypothesis that the PWE increases acceptance of inequality. A correlational study found that the more people endorsed PWE, the less disturbed they were about factual statistics about wealth equality in the US. Two experiments found that exposing people to PWE items decreased their disturbance with income inequality. The findings indicate that machine learning models can be reused to generate viable hypotheses.
46Human-Robot Confluence: Toward a Humane Robotics.PubMed
Giuseppe Riva, Brenda K Wiederhold
Cyberpsychol Behav Soc Netw. 2021 May;24(5):291-293. doi: 10.1089/cyber.2021.29215.gri.
The new humanoid robots not only perform tasks, but also can activate interactions and social relationships with other robots and with humans. In this view, the diffusion of humanoid robots with a physical structure reminiscent of the human body, endowed with decision-making abilities, and capable of externalizing and generating emotions, is opening a new line of research with a main objective of understanding the dynamics of social interactions generated by the encounters between robots and humans. However, this process is not easy. To be accepted by society, robots have to "understand" people and to adapt themselves to complex real-life social environments. This goal underlines the importance for research of aspects such as communication, acceptance, and ethics that require the collaboration between multiple disciplines, including psychology, neuroscience, design, mechatronics, computer science, philosophy, sociology, anthropology, biomechanics, and roboethics. This special issue seeks to gather knowledge from these disciplines with respect to human-robot confluence (HRC) in the application of robots in everyday life, including robot training partners and industrial collaborative robots (Cobots). It covers a wide range of topics related to HRC, involving theories, methodologies, technologies, and empirical and experimental studies. The final goal is to support researchers and developers in creating robots that not only have a humanoid body but that are really "humane": accessible, sympathetic, generous, compassionate, and forbearing.
47Artificial intelligence in medicine: Ethical, social and legal perspectives.PubMed
Joseph Sung
Ann Acad Med Singap. 2023 Dec 28;52(12):695-699. doi: 10.47102/annals-acadmedsg.2023103.
Artificial intelligence (AI) has permeated into every aspect of medicine and promises to provide accurate diagnosis, better management decision and improved outcome for patients and healthcare system. However, ethical, social and legal issues need to be resolved for successful implementation of AI tools in clinical practice. In order to gain trust and acceptance, AI algorithms should offer maximum explainability and inclusiveness. Robust evidence of benefit to patients and healthcare services has to be provided to gain justification of using these tools. Doctor-patient relationship needs to be maintained in order to gain trust and acceptance of users. Autonomy of decisions and dignity of patients need to be preserved while using machine in healthcare. Responsibility and accountability in the use of AI in medicine should be deliberated and defined before mishaps and damage occur. A new role of healthcare providers will emerge with the advancement of technology and changes are inevitable. This manuscript is based on the Gordon Arthur Ransome Lecture 2022 entitled "Artificial Intelligence in Medicine: Ethical, Social and Legal Perspective". It represents the opinion of the orator.
48The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions.PubMed
Aurelia Sauerbrei, Angeliki Kerasidou, Federica Lucivero, et al.
BMC Med Inform Decis Mak. 2023 Apr 20;23(1):73. doi: 10.1186/s12911-023-02162-y.
Artificial intelligence (AI) is often cited as a possible solution to current issues faced by healthcare systems. This includes the freeing up of time for doctors and facilitating person-centred doctor-patient relationships. However, given the novelty of artificial intelligence tools, there is very little concrete evidence on their impact on the doctor-patient relationship or on how to ensure that they are implemented in a way which is beneficial for person-centred care.Given the importance of empathy and compassion in the practice of person-centred care, we conducted a literature review to explore how AI impacts these two values. Besides empathy and compassion, shared decision-making, and trust relationships emerged as key values in the reviewed papers. We identified two concrete ways which can help ensure that the use of AI tools have a positive impact on person-centred doctor-patient relationships. These are (1) using AI tools in an assistive role and (2) adapting medical education. The study suggests that we need to take intentional steps in order to ensure that the deployment of AI tools in healthcare has a positive impact on person-centred doctor-patient relationships. We argue that the proposed solutions are contingent upon clarifying the values underlying future healthcare systems.
49Ethical Considerations of Using ChatGPT in Health Care.PubMed
Changyu Wang, Siru Liu, Hao Yang, et al.
J Med Internet Res. 2023 Aug 11;25:e48009. doi: 10.2196/48009.
ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.
51AI and culture: Culturally dependent responses to AI systems.PubMed
Aaron J Barnes, Yuanyuan Zhang, Ana Valenzuela
Curr Opin Psychol. 2024 Aug;58:101838. doi: 10.1016/j.copsyc.2024.101838. Epub 2024 Jul 1.
This article synthesizes recent research connected to how cultural identity can determine responses to artificial intelligence. National differences in AI adoption imply that culturally-driven psychological differences may offer a nuanced understanding and interventions. Our review suggests that cultural identity shapes how individuals include AI in constructing the self in relation to others and determines the effect of AI on key decision-making processes. Individualists may be more prone to view AI as external to the self and interpret AI features to infringe upon their uniqueness, autonomy, and privacy. In contrast, collectivists may be more prone to view AI as an extension of the self and interpret AI features to facilitate conforming to consensus, respond to their environment, and protect privacy.
52Empowering biomedical discovery with AI agents.PubMed
Shanghua Gao, Ada Fang, Yepeng Huang, et al.
Cell. 2024 Oct 31;187(22):6125-6151. doi: 10.1016/j.cell.2024.09.022.
We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
53Generative artificial intelligence, human creativity, and art.PubMed
Eric Zhou, Dokyun Lee
PNAS Nexus. 2024 Mar 5;3(3):pgae052. doi: 10.1093/pnasnexus/pgae052. eCollection 2024 Mar.
Recent artificial intelligence (AI) tools have demonstrated the ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g. Midjourney, Stable Diffusion, DALL-E), which automates humans' artistic execution to generate digital artworks. Utilizing a dataset of over 4 million artworks from more than 50,000 unique users, our research shows that over time, text-to-image AI significantly enhances human creative productivity by 25% and increases the value as measured by the likelihood of receiving a favorite per view by 50%. While peak artwork Content Novelty, defined as focal subject matter and relations, increases over time, average Content Novelty declines, suggesting an expanding but inefficient idea space. Additionally, there is a consistent reduction in both peak and average Visual Novelty, captured by pixel-level stylistic elements. Importantly, AI-assisted artists who can successfully explore more novel ideas, regardless of their prior originality, may produce artworks that their peers evaluate more favorably. Lastly, AI adoption decreased value capture (favorites earned) concentration among adopters. The results suggest that ideation and filtering are likely necessary skills in the text-to-image process, thus giving rise to "generative synesthesia"-the harmonious blending of human exploration and AI exploitation to discover new creative workflows.
54Loneliness, Theory of Mind, and Artificial Intelligence.PubMed
George S Alexopoulos
Am J Geriatr Psychiatry. 2025 Oct;33(10):1111-1118. doi: 10.1016/j.jagp.2025.06.015. Epub 2025 Jun 20.
Philosophy and neurobiology can guide the integration of smart machines in initiatives intended to address the epidemic of loneliness in the aging population. Behaviorism and functionalism assume that AI systems can develop empathy-like functions. Property dualism posits that consciousness is an emergent function that depends on, but supervenes, its underlying neurobiological functions. Emergent functions occur in biology and, theoretically, they may occur in artificial systems. Therefore, advanced AI systems may develop intentionality, and agency, functions beyond those planned by their designers. Therefore, guidelines are needed for protection of humans and especially older adults with cognitive and cultural limitations related to technology. For AI systems to be helpful in mitigating loneliness, they should have capabilities similar to those of theory of mind functions (ToM), that allow an individual to identify emotions, intentions, and wishes of him/herself and of others and recognize to whom they belong. Cognitive and neurobiological studies show that ToM are disrupted in persons experiencing loneliness. AI systems with ToM functions may produce empathy-like responses appropriate for mitigating loneliness. Smart technology can be most effective against loneliness if integrated in the fabric of social and clinical interventions. Acceptance of technology by geriatric clinicians is critical, because they are best equipped to guide the targeted use of smart machines, prevent complications (e.g. countertherapeutic transference towards the machine), and correct the course of interventions. Neurobiology related to ToM functions and to prolonged grief disorder may guide research examining whether digital interventions can change neural functions related to loneliness and psychopathology.
55Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden.PubMed
Shreya J Shah, Anna Devon-Sand, Stephen P Ma, et al.
J Am Med Inform Assoc. 2025 Feb 1;32(2):375-380. doi: 10.1093/jamia/ocae295.
OBJECTIVE: This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout. MATERIALS AND METHODS: This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings. RESULTS: Paired survey analysis (n = 38) revealed large statistically significant reductions in task load (-24.42, p <.001) and burnout (-1.94, p <.001), and moderate statistically significant improvements in usability scores (+10.9, p <.001). Post-survey responses (n = 46) indicated favorable utility with improved perceptions of efficiency, documentation quality, and ease of use. DISCUSSION: In one of the first pilot implementations of ambient AI scribe technology, improvements in physician task load, burnout, and usability were demonstrated. CONCLUSION: Ambient AI scribes like DAX Copilot may enhance clinical workflows. Further research is needed to optimize widespread implementation and evaluate long-term impacts.
56The Effect of Ambient Artificial Intelligence Notes on Provider Burnout.PubMed
Jason Misurac, Lindsey A Knake, James M Blum
Appl Clin Inform. 2025 Mar;16(2):252-258. doi: 10.1055/a-2461-4576. Epub 2024 Nov 5.
BACKGROUND: Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be reviewed by clinicians. OBJECTIVE: To assess provider burnout and professional fulfillment associated with ambient AI technology during a pilot study, assessed using the Stanford Professional Fulfillment Index (PFI). METHODS: A pre-post observational study was conducted at University of Iowa Health Care with 38 volunteer physicians and advanced practice providers. Participants used a commercial ambient AI tool over a 5-week trial in ambulatory environments. The AI tool transcribed patient-clinician conversations and generated preliminary clinical notes for review and entry into the electronic medical record. Burnout and professional fulfillment were assessed using the Stanford PFI at baseline and postintervention. RESULTS: Pre- and posttest surveys were completed by 35/38 participants (92% survey completion rate). Results showed a significant reduction in burnout scores, with the median burnout score improving from 4.16 to 3.16 ( = 0.005), with validated Stanford PFI cut-off for overall burnout of 3.33. Burnout rates decreased from 69 to 43%. There was a notable improvement in interpersonal disengagement scores (3.6 vs. 2.5, < 0.001), although work exhaustion scores did not change significantly. Professional fulfillment showed a modest, nonsignificant upward trend (6.1 vs. 6.5, = 0.10). CONCLUSION: Ambient AI significantly reduces healthcare provider burnout and may enhance professional fulfillment. By alleviating documentation burdens, ambient AI can improve operational efficiency and provider well-being. These findings suggest that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.
57The impact of artificial intelligence on women's empowerment, and work-life balance in Saudi educational institutions.PubMed
Sayeda Meharunisa, Hawazen Almugren, Masahina Sarabdeen, et al.
Front Psychol. 2024 Sep 30;15:1432541. doi: 10.3389/fpsyg.2024.1432541. eCollection 2024.
Gender prejudice and stereotypes are prevalent in the workplace, particularly for women in the Artificial Intelligence (AI) industry, where they can significantly hinder professional development and limit prospects for growth. These challenges contribute to the underrepresentation of executives in AI. However, with the right measures, these barriers can be overcome, leading to a more inclusive and diverse AI industry. Women in this demanding technological domain often face additional difficulties in achieving a work-life balance, further constraining their professional advancement and engagement in the industry. This research aims to examine the implications of AI capabilities on work-life balance and the empowerment of female faculty members in enhancing the efficiency of educational institutions. The research performs a structural equation modeling (SEM) approach, using a survey conducted on female faculty of Saudi Arabian universities. The study specifically considers moderating variables such as age, education level, experience, and marital status. The findings, which reveal that AI managerial capability, as well as AI infrastructure agility, impacts work-life balance and empowerment of women faculties in educational institution efficiency, underscore the significance of considering demographic factors when analyzing women's empowerment and work-life balance as outcomes. By exploring these factors, the research provides a comprehensive understanding of how AI capabilities impact women's empowerment and their ability to maintain a work-life balance, ultimately contributing to the efficiency and effectiveness of educational institutions. These results emphasize the value of increasing women's empowerment and raising the standard of performance evaluation systems in educational sectors.
58Evaluating the impact of China's educational policy on career development: A comprehensive analysis of university education's role using machine learning (ML) modeling.PubMed
Qiankun Yang, Changyong Liang
Acta Psychol (Amst). 2025 Oct;260:105398. doi: 10.1016/j.actpsy.2025.105398. Epub 2025 Sep 2.
The impact of university education on the comprehensive development of students in higher education is a crucial area of research. This study shows the effect of China's recently released Guideline of Educational Policy (GCEP) on the post-graduation career development of students. Using linear regression analysis, the study first explored the relationship between well-rounded educational development and students' subsequent career progression. Additionally, a three-stage model was proposed to investigate the influence of moral, intellectual, physical, aesthetic, and vocational education on the long-term career development of university students. The results indicate that the development of moral, intellectual, physical, aesthetic, and vocational education had a statistically significant positive impact on students' career progression. Differentiated effects were observed across the three stages of career development, namely the survival stage, the growth stage, and the self-realization stage. Based on our findings, some specific suggestions of practical significance and academic value are proposed for education policy-makers at the national, university, enterprise, and student levels. In this article, an artificial neural network (ANN) was used to better understand the relationships between various parameters. Neural networks are a practical method for learning complex functions, and are immune to training data errors, making them useful for problems like speech and image recognition. We used the neural network to estimate the survival, growth, and self-improvement stages by adjusting factors like education and work. The neural network's predictions were then validated against experimental results using linear regression, confirming acceptable error levels.
59How artificial intelligence affects the labour force employment structure from the perspective of industrial structure optimisation.PubMed
Xiaowen Wang, Mingyue Chen, Nanxu Chen
Heliyon. 2024 Feb 22;10(5):e26686. doi: 10.1016/j.heliyon.2024.e26686. eCollection 2024 Mar 15.
To investigate how artificial intelligence (AI) affects the structure of labour force employment, we integrate robotics adoption and employment into this study's model. Based on Chinese provincial panel data from 2010 to 2019, fixed, mediating and threshold effects models and a spatial heterogeneity model were used to empirically test the impact of AI on the employment structure from the perspective of industrial structure optimisation and its mechanisms of action. The findings demonstrate that the impact of AI on the labour force employment structure reflects unique characteristics for China and promotes the advancement of the nation's employment structure. The influence of AI on the labour force employment structure follows a non-linear pattern, fostering labour force employment structure optimisation and upgrading from the perspective of industrial structure optimisation. Further investigation reveals the influence of spatial spillover effects from AI on employment structure optimisation. These research findings have theoretical value and practical significance for optimising China's employment structure in the context of AI.
60Maternity protection for female non-standard workers in South Africa: the case of domestic workers.PubMed
Catherine Pereira-Kotze, Tanya Doherty, Mieke Faber
BMC Pregnancy Childbirth. 2022 Aug 22;22(1):657. doi: 10.1186/s12884-022-04944-0.
BACKGROUND: Many women work in positions of non-standard employment, with limited legal and social protection. Access to comprehensive maternity protection for all working women could ensure that all women and children can access health and social protection. This study aimed to describe the maternity protection benefits available to women in positions of non-standard employment in South Africa, using domestic workers as a case study. METHODS: A qualitative descriptive study design was used. National policy documents containing provisions on maternity protection were identified and analysed. Interviews were conducted with purposively selected key informants. Data extracted from published policy documents and information obtained from interviews were triangulated. A thematic analysis approach was used for evaluation of policy content and analysis of the interviews. RESULTS: Twenty-nine policy and legislative documents were identified that contain provisions on maternity protection relevant to non-standard workers. These documents together with three key informant interviews and two media releases are used to describe availability and accessibility of maternity protection benefits for non-standard workers in South Africa, using domestic workers as a case study. Maternity protection is available in South Africa for some non-standard workers. However, the components of maternity protection are dispersed through many policy documents and there is weak alignment within government on maternity protection. Implementation, monitoring, and enforcement of existing maternity protection policy is inadequate. It is difficult for non-standard workers to access maternity protection benefits, particularly cash payments. Some non-standard workers have unique challenges in accessing maternity protection, for example domestic workers whose place of work is a private household and therefore difficult to monitor. CONCLUSION: The heterogeneity of non-standard employment makes it challenging for many women to access maternity protection. There are policy amendments that could be made and improvements to policy implementation that would enhance non-standard workers' access to maternity protection. Potential long-term benefits to women and children's health and development could come from making comprehensive maternity protection available and accessible to all women.
61Labour and social protection gaps impacting the health and well-being of workers in non-standard employment: An international comparative study.PubMed
Signild Kvart, Isabel Cuervo, Virginia Gunn, et al.
PLoS One. 2025 Mar 25;20(3):e0320248. doi: 10.1371/journal.pone.0320248. eCollection 2025.
BACKGROUND: World economies increasingly rely on non-standard employment arrangements, which has been linked to ill health. While work and employment conditions are recognized structural determinants of health and health equity, policies aiming to protect workers from negative implications predominantly focus on standard employment arrangements and the needs of workers in non-standard employment may be neglected. The aim of this study is to explore workers' experiences of gaps in labour regulations and social protections and its influence on their health and well-being across 6 countries with differing policy approaches: Belgium, Canada, Chile, Spain, Sweden, and the United States. METHODS: 250 semi-structured interviews with workers in non-standard employment were analyzed thematically using a multiple case-study approach. RESULTS: There are notable differences in workers' rights to protection across the countries. However, participants across all countries experienced similar challenges including employment instability, income inadequacy and limited rights and protection, due to policy-related gaps and access-barriers. In response, they resorted to individual resources and strategies, struggled to envision supportive policies, and expressed low expectations of changes by employers and policymakers. CONCLUSIONS: Policy gaps threaten workers' health and well-being across all study countries, irrespective of the levels of labour market regulations and social protections. Workers in non-standard employment disproportionately endure economic risks, which may increase social and health inequality. The study highlights the need to improve social protection for this vulnerable population.
62Who loses more? Identifying the relationship between hospitalization and income loss: prediction of hospitalization duration and differences of gender and employment status.PubMed
Minsung Sohn, Daseul Moon, Patricia O'Campo, et al.
BMC Public Health. 2022 Feb 4;22(1):232. doi: 10.1186/s12889-022-12647-6.
BACKGROUND: The major determinants of health and well-being include wider socio-economic and political responses to poverty alleviation. To data, however, South Korea has no related social protection policies to replace income loss or prevent non-preferable health conditions for workers. In particular, there are several differences in social protection policies by gender or occupational groups. This study aimed to investigate how hospitalization affects income loss among workers in South Korea. METHODS: The study sample included 4876 Korean workers who responded to the Korean Welfare Panel Study (KoWePS) for all eight years from 2009 to 2016. We conducted a receiver operating characteristics (ROC) analysis to determine the cut-off point for the length of hospitalization that corresponded to the greatest loss of income. We used panel multi-linear regression to examine the relationship between hospitalization and income loss by gender and employment arrangement. RESULTS: The greatest income loss for women in non-standard employment and self-employed men was observed when the length of hospitalization was seven days or less. When they were hospitalized for more than 14 days, income loss also occurred among men in non-standard employment. In addition, when workers were hospitalized for more than 14 days, the impact of the loss of income was felt into the subsequent year. CONCLUSION: Non-standard and self-employed workers, and even female standard workers, are typically excluded from public insurance coverage in South Korea, and social security is insufficient when they are injured. To protect workers from the vicious circle of the poverty-health trap, national social protections such as sickness benefits are needed.
63A Systematic Review of Evaluated Labor Market Initiatives Addressing Precarious Employment: Findings and Public Health Implications.PubMed
Virginia Gunn, Nuria Matilla-Santander, Bertina Kreshpaj, et al.
Int J Soc Determinants Health Health Serv. 2025 Jul;55(3):268-288. doi: 10.1177/27551938241310120. Epub 2025 Jan 15.
Precarious employment (PE) is a major determinant of population health and contributor to health and social inequities. The purpose of this article is to synthesize and critically appraise available evidence on labor market initiatives addressing PE identified through a systematic review. Of the 21 initiatives reviewed, grouped into four categories-labor market policies, legislation, and reforms; union strategies; apprenticeships and other youth programs; social protection programs-10 showed consistently positive outcomes and 11 a combination of negative, mixed, or inconclusive outcomes. In addition to reviewing the key findings, we discuss public health implications and recommendations related to PE and the implementation and evaluation of initiatives. Given the wide diversity of initiatives, implementation approaches, evaluation methods, and socioeconomic and historical contexts characterizing the labor markets of the countries studied, we refrain from making recommendations regarding the most effective initiatives to address PE. Instead, we discuss several implications concerning the four types of initiatives to further support those searching for solutions to address PE. We strongly recommend tailoring adopted initiatives to local contexts to match a country's specific PE problems and unique labor market and socioeconomic context.
64Nursing-centered development of an AI-based decision support system in pressure ulcer and incontinence-associated dermatitis management - a mixed methods study.PubMed
Khalid Majjouti, Vanessa Priester, Michaela Tapp-Herrenbrueck, et al.
BMC Nurs. 2025 Jul 1;24(1):808. doi: 10.1186/s12912-025-03448-4.
BACKGROUND: Differentiating between stage 1 or 2 pressure ulcer/pressure injury (PU/PI) and incontinence-associated dermatitis (IAD) poses a significant challenge for healthcare professionals, due to their visual similarity. Incorrect assessments may trigger inappropriate interventions, potentially resulting in delayed treatment. KIADEKU is a multi-center research project aimed at supporting the assessment and documentation of PU/PI and IAD, as well as the implementation of evidence-based care through an AI-based application in nursing care. This paper investigates how to integrate evidence from nursing science and clinical practice into the development of the proposed AI system. METHODS: We conducted a literature review of nursing criteria for wound assessment. Nursing experts iteratively evaluated the findings, leading to the definition of a Minimum Data Set (MDS) that the research team used to annotate wound images for AI training. We collected a data set of wound images from the medical records of two university hospitals. To ensure high data quality, we implemented a validation process involving up to four independent expert assessments of each wound image. We calculated Krippendorff's alpha to assess the internal consistency of the annotation process for reliability analysis. This study adhered to the TRIPOD-AI guidelines. RESULTS: The differentiation between PU/PI and IAD primarily relies on clinical observation and visual inspection, with key factors including aetiology, anatomical location, and wound morphology. The validated MDS encompasses 18 wound-related and four aetiological categories, including visual and contextual patient data. The AI system consequently integrates wound images with categorical patient information. The reliability analysis of 1,521 annotated wound images indicates substantial agreement for wound type classification (α = 0.64, 95% CI 0.62-0.68) and fair to moderate agreement for PU/PI (α = 0.57, 95% CI 0.55-0.63) and IAD categorization (α = 0.27, 95% CI 0.20-0.36). CONCLUSIONS: The integration of evidence from nursing science and practice into the AI development process using a mixed-methods approach, established a robust, evidence-based foundation. This approach yielded an innovative implementation of routine care data for AI training, advancing the field of AI-driven wound care solutions. TRIAL REGISTRATION: Registered with the German Clinical Trials Register (DRKS) on 2023-09-05. DRKS-ID: DRKS00029961.
65Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study.PubMed
Alexander Brehmer, Constantin Seibold, Jan Egger, et al.
JMIR AI. 2025 May 1;4:e67356. doi: 10.2196/67356.
BACKGROUND: Pressure ulcers (PUs) and incontinence-associated dermatitis (IAD) are prevalent conditions in clinical settings, posing significant challenges due to their similar presentations but differing treatment needs. Accurate differentiation between PUs and IAD is essential for appropriate patient care, yet it remains a burden for nursing staff and wound care experts. OBJECTIVE: This study aims to develop and introduce a robust multimodal deep learning framework for the classification of PUs and IAD, along with the fine-grained categorization of their respective wound severities, to enhance diagnostic accuracy and support clinical decision-making. METHODS: We collected and annotated a dataset of 1555 wound images, achieving consensus among 4 wound experts. Our framework integrates wound images with categorical patient data to improve classification performance. We evaluated 4 models-2 convolutional neural networks and 2 transformer-based architectures-each with approximately 25 million parameters. Various data preprocessing strategies, augmentation techniques, training methods (including multimodal data integration, synthetic data generation, and sampling), and postprocessing approaches (including ensembling and test-time augmentation) were systematically tested to optimize model performance. RESULTS: The transformer-based TinyViT model achieved the highest performance in binary classification of PU and IAD, with an F1-score (harmonic mean of precision and recall) of 93.23%, outperforming wound care experts and nursing staff on the test dataset. In fine-grained classification of wound categories, the TinyViT model also performed best for PU categories with an F1-score of 75.43%, while ConvNeXtV2 showed superior performance in IAD category classification with an F1-score of 53.20%. Incorporating multimodal data improved performance in binary classification but had less impact on fine-grained categorization. Augmentation strategies and training techniques significantly influenced model performance, with ensembling enhancing accuracy across all tasks. CONCLUSIONS: Our multimodal deep learning framework effectively differentiates between PUs and IAD, achieving high accuracy and outperforming human wound care experts. By integrating wound images with categorical patient data, the model enhances diagnostic precision, offering a valuable decision-support tool for health care professionals. This advancement has the potential to reduce diagnostic uncertainty, optimize treatment pathways, and alleviate the burden on medical staff, leading to faster interventions and improved patient outcomes. The framework's strong performance suggests practical applications in clinical settings, such as integration into hospital electronic health record systems or mobile applications for bedside diagnostics. Future work should focus on validating real-world implementation, expanding dataset diversity, and refining fine-grained classification capabilities to further enhance clinical utility.
66Valuable insights into general practice staff's experiences and perspectives on AI-assisted diabetic retinopathy screening-An interview study.PubMed
Malene Krogh, Malene Hentze, Morten Sig Ager Jensen, et al.
Front Med (Lausanne). 2025 Mar 11;12:1565532. doi: 10.3389/fmed.2025.1565532. eCollection 2025.
AIM: This study explores the hands-on experiences and perspectives of general practice staff regarding the feasibility of conducting artificial intelligence-assisted (AI-assisted) diabetic retinopathy screenings (DRS) in general practice settings. METHOD: The screenings were tested in 12 general practices in the North Denmark Region and were conducted as part of daily care routines over ~4 weeks. Subsequently, 21 staff members involved in the DRS were interviewed. RESULTS: Thematic analysis generated four main themes: (1) Experiences with DRS in daily practice, (2) Effective DRS implementation in general practice in the future, (3) Trust and approval of AI-assisted DRS in general practice, and (4) Implications of DRS in general practice. The findings suggest that general practice staff recognise the potential for AI-assisted DRS to be integrated into their clinical workflows. However, they also emphasise the importance of addressing both practical and systemic factors to ensure successful implementation of DRS within the general practice setting. CONCLUSION: Focusing on the practical experiences and perspectives of general practice staff, this study lays the groundwork for future research aimed at optimising the implementation of AI-assisted DRS in general practice settings, while recognising that the insights gained may also inform broader primary care contexts.
67Evaluation of End-User Participation in Artificial Intelligence Nursing Projects.PubMed
Alessia Nowak, Rahel Gubser, Akira-Sebastian Poncette, et al.
Stud Health Technol Inform. 2024 Aug 22;316:1108-1109. doi: 10.3233/SHTI240604.
Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Active participation of end-users in the development and implementation of AI can enhance acceptance. This study proposes a scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers. It employs the qualitative-analytical COARSE method for scale development and evaluation. The instrument includes 11 items across two sub-scales: activities for active participation of end-users and empowerment activities. It highlights the importance of the measurement's purpose and consequences for interpreting the results of the evaluated degree of end-user participation. The study points to future research opportunities, underscored by the need for psychometric validation, such as reliability and validity.
68Artificial Intelligence user interface preferences in radiology: A scoping review.PubMed
Avneet Gill, Clare Rainey, Laura McLaughlin, et al.
J Med Imaging Radiat Sci. 2025 May;56(3):101866. doi: 10.1016/j.jmir.2025.101866. Epub 2025 Feb 27.
INTRODUCTION/BACKGROUND: Modern forms of Artificial intelligence (AI) have developed in radiology over the past few years. With the current workforce shortages, in both radiology and radiography professions, AI continues to prove its place in supporting clinical radiology processes. The aim of the scoping review was to investigate the existing literature on the topic of preference of use of artificial intelligence interfaces within a radiology context. METHODS: Using a systematic approach, papers were chosen against an inclusion criterion of addressing radiological AI user interface preferences to be included in the scoping review. Arksey O'Malley's and Levac's framework were used to inform the procedural steps for the scoping review. Four databases were searched including MEDLINE Ovid, Scopus, Web of Science and Engineering Village. Reliability was improved through the involvement of three researchers to select the papers against the inclusion criteria. RESULTS: Six papers were identified to fit the inclusion criteria of radiological AI user interface preferences. These varied methodologically including two observational studies, two simulated user testing studies, a diagnostic accuracy study and a multi-case study. AI user interfaces were evaluated in two studies. Mixed responses were obtained with some alignment in preference for heatmap image overlays and highly detailed user interfaces are linked to higher preference amongst users. Limited literature exists on AI user interfaces and a lack of research evaluating current AI interface preference, either in post or pre-deployment. DISCUSSION: The mix of methods used within studies indicated that there is not yet a standardised method for assessing AI tool design and preference within radiology, with common use of a System Usability Scale survey tool in conjunction with another method. There was also a varied response when considering the preferred user interface in radiology, though simple, non-complicated designs were suggested to be ideal by participants. CONCLUSION: Medical imaging AI user interface research is essential for the acceptability of AI technology into radiology departments. This scoping review identified the current landscape of AI user interface research within a radiology setting. There is a requirement for more radiology AI research focussing on end user or imaging professional involvement and their preferences. There is an explicit need for further research in the field, due to the lack of standardised outcome measures, lack clear findings regarding ideal user interfaces and lack of inclusion of radiographers. The dearth of studies including radiographers and small sample sizes of participants within these studies identifies the mindset shift required for radiology, and AI vendors alike.
69Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.PubMed
Reabal Najjar
Diagnostics (Basel). 2023 Aug 25;13(17):2760. doi: 10.3390/diagnostics13172760.
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.
70Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.PubMed
Olivia Prior, Carlos Macarro, Víctor Navarro, et al.
Radiol Artif Intell. 2024 Mar;6(2):e230118. doi: 10.1148/ryai.230118.
Purpose To identify precise three-dimensional radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL of ICC ≥ 0.50). Habitats were obtained with Gaussian mixture models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI, and tumor biopsies. Results Three-dimensional radiomics showed poor repeatability (LCL of ICC: median [IQR], 0.442 [0.312-0.516]) and poor reproducibility against kernel radius (LCL of ICC: median [IQR], 0.440 [0.33-0.526]) but excellent reproducibility against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]). Twenty-six radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC: median [IQR], 0.601 [0.494-0.712] and 0.651 [0.52-0.784] for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC: median [IQR], 0.532 [0.424-0.637] and 0.587 [0.465-0.703] for lung and liver lesions, respectively; < .001). In the case study, CT habitats correlated quantitatively and qualitatively with heterogeneity observed in multiparametric MRI habitats and histology. Conclusion Precise three-dimensional radiomics features were identified on CT images that enabled tumor heterogeneity assessment through stable tumor habitat computation. CT, Diffusion-weighted Imaging, Dynamic Contrast-enhanced MRI, MRI, Radiomics, Unsupervised Learning, Oncology, Liver, Lung . © RSNA, 2024 See also the commentary by Sagreiya in this issue.
71[Using Artificial Intelligence Software for Diagnosing Emphysema and Interstitial Lung Disease].PubMed
Sang Hyun Paik, Gong Yong Jin
J Korean Soc Radiol. 2024 Jul;85(4):714-726. doi: 10.3348/jksr.2024.0050. Epub 2024 Jul 30.
Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.
72A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study.PubMed
Bin Hu, Zhao Shi, Li Lu, et al.
Lancet Digit Health. 2024 Apr;6(4):e261-e271. doi: 10.1016/S2589-7500(23)00268-6.
BACKGROUND: Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation. METHODS: We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases. FINDINGS: The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001). INTERPRETATION: This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases. FUNDING: National Natural Science Foundation of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.
73Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study.PubMed
Dennis Robert, Saigopal Sathyamurthy, Anshul Kumar Singh, et al.
Acad Radiol. 2025 Mar;32(3):1706-1717. doi: 10.1016/j.acra.2024.11.003. Epub 2024 Nov 25.
RATIONALE AND OBJECTIVES: Missed nodules in chest radiographs (CXRs) are common occurrences. We assessed the effect of artificial intelligence (AI) as a second reader on the accuracy of radiologists and non-radiology physicians in lung nodule detection and localization in CXRs. MATERIALS AND METHODS: This retrospective study using the multi-reader multi-case design included 300 CXRs acquired from 40 hospitals across the US. All CXRs had a paired follow-up image (chest CT or CXR) to augment the ground truth establishment for the presence and location of nodules on CXRs by five independent thoracic radiologists. 15 readers (nine radiologists and six non-radiology physicians) read each CXR twice in a second-reader paradigm, once without AI and then immediately with AI assistance. The primary analysis assessed the difference in area-under-the-alternative-free-response-receiver-operating-characteristic-curve (AFROC) of readers with and without AI. Case-level area-under-the-receiver-operating-characteristic-curve (AUROC), sensitivity, and specificity were assessed in secondary analyses. RESULTS: A total of 300 CXRs (147 with nodules, 153 without nodules) from 300 patients (mean age, 64 years ± 15 [standard deviation]; 174 women) were included. The mean AFROC of readers was 0.73 without AI and 0.81 with AI (95% CI of difference, 0.05-0.10). Case-level AUROC was 0.77 without AI and 0.84 with AI (95% CI of difference, 0.04-0.09). Case-level sensitivity was 72.8% and 83.5% (95% CI of difference, 6.8-14.6) and specificity was 71.1% and 72.0% (95% CI of difference, -0.8-2.6) without and with AI, respectively. CONCLUSION: Using AI, readers detected and localized more nodules without any significant difference in false positive interpretations.
74Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning.PubMed
Xiuyuan Chen, Chenyang Dai, Muyun Peng, et al.
Nat Commun. 2025 May 1;16(1):4086. doi: 10.1038/s41467-025-59200-8.
The increasing complexity of lung surgeries necessitates the need for enhanced imaging support to improve the precision and efficiency of preoperative planning. Despite the promise of 3D reconstruction, clinical adoption remains limited due to time constraints and insufficient validation. To address this, we evaluate an artificial intelligence-driven 3D reconstruction system for pulmonary vessels and bronchi in a retrospective, multi-center multi-reader multi-case study. Using a two-stage crossover design, ten thoracic surgeons assess 140 cases with and without the system's assistance. The system significantly improves the accuracy of anatomical variant identification by 8% (p < 0.01), reducing errors by 41%. Improvements in secondary endpoints are also observed. Operation procedure selection accuracy is improved by 8%, with a 35% decrease in errors. Preoperative planning time is decreased by 25%, and user satisfaction is high at 99%. These benefits are consistent across surgeons of varying experience. In conclusion, the artificial intelligence-driven 3D reconstruction system significantly improves the identification of anatomical variants, addressing a critical need in preoperative planning for thoracic surgery.
75System-Wide Leadership.PubMed
Deborah Sesok-Pizzini, Mark-Alan Pizzini
Clin Lab Med. 2025 Sep;45(3):519-530. doi: 10.1016/j.cll.2025.04.009. Epub 2025 Jun 9.
The article explores system-wide leadership, emphasizing the transition from functional to C-suite roles. Effective leadership requires fostering positive environments, leveraging emotional intelligence, and aligning organizational goals with a purpose-driven culture. Key areas of focus include adapting communication styles to build trust, embracing risk and failure to drive innovation, and fostering psychological safety to enhance team performance. The article highlights the importance of building diverse, high-performing teams and leveraging tools like artificial intelligence for decision-making. By prioritizing inclusivity, emotional intelligence, and resilience, leaders can create cohesive organizations that thrive in dynamic and complex environments.
76Reshaping Organizational Culture in Pathology.PubMed
Darryl Elzie
Clin Lab Med. 2025 Sep;45(3):383-396. doi: 10.1016/j.cll.2025.04.002. Epub 2025 May 24.
"Reshaping Pathology Culture" explores the transformation needed in pathology departments to meet the demands of modern health care. It advocates a shift from traditional hierarchical models to collaborative leadership, uniting cross-generational pathologists and technologists. Embracing advanced technologies like artificial intelligence and digital pathology requires a parallel cultural evolution to optimize diagnostic accuracy and efficiency. The article highlights the importance of continuous education and adaptability for thriving in a dynamic clinical and regulatory landscape. Additionally, fostering diversity and inclusion is emphasized as a cornerstone for creating a culture that values diverse perspectives and supports professional growth for all team members.
77An Artificial Intelligence Training Workshop for Diagnostic Radiology Residents.PubMed
Ricky Hu, Arsalan Rizwan, Zoe Hu, et al.
Radiol Artif Intell. 2023 Feb 8;5(2):e220170. doi: 10.1148/ryai.220170. eCollection 2023 Mar.
PURPOSE: To develop, implement, and evaluate feedback for an artificial intelligence (AI) workshop for radiology residents that has been designed as a condensed introduction of AI fundamentals suitable for integration into an existing residency curriculum. MATERIALS AND METHODS: A 3-week AI workshop was designed by radiology faculty, residents, and AI engineers. The workshop was integrated into curricular academic half-days of a competency-based medical education radiology training program. The workshop consisted of live didactic lectures, literature case studies, and programming examples for consolidation. Learning objectives and content were developed for foundational literacy rather than technical proficiency. Identical prospective surveys were conducted before and after the workshop to gauge the participants' confidence in understanding AI concepts on a five-point Likert scale. Results were analyzed with descriptive statistics and Wilcoxon rank sum tests to evaluate differences. RESULTS: Twelve residents participated in the workshop, with 11 completing the survey. An average score of 4.0 ± 0.7 (SD), indicating agreement, was observed when asking residents if the workshop improved AI knowledge. Confidence in understanding AI concepts increased following the workshop for 16 of 18 (89%) comprehension questions ( value range: .001 to .04 for questions with increased confidence). CONCLUSION: An introductory AI workshop was developed and delivered to radiology residents. The workshop provided a condensed introduction to foundational AI concepts, developed positive perception, and improved confidence in AI topics. Medical Education, Machine Learning, Postgraduate Training, Competency-based Medical Education, Medical Informatics © RSNA, 2023.
78Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review.PubMed
Fatima Nagi, Rawan Salih, Mahmood Alzubaidi, et al.
Stud Health Technol Inform. 2023 Jun 29;305:648-651. doi: 10.3233/SHTI230581.
Artificial Intelligence (AI) is increasingly used to support medical students' learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.
79AI in medical education: medical student perception, curriculum recommendations and design suggestions.PubMed
Qianying Li, Yunhao Qin
BMC Med Educ. 2023 Nov 9;23(1):852. doi: 10.1186/s12909-023-04700-8.
Medical AI has transformed modern medicine and created a new environment for future doctors. However, medical education has failed to keep pace with these advances, and it is essential to provide systematic education on medical AI to current medical undergraduate and postgraduate students. To address this issue, our study utilized the Unified Theory of Acceptance and Use of Technology model to identify key factors that influence the acceptance and intention to use medical AI. We collected data from 1,243 undergraduate and postgraduate students from 13 universities and 33 hospitals, and 54.3% reported prior experience using medical AI. Our findings indicated that medical postgraduate students have a higher level of awareness in using medical AI than undergraduate students. The intention to use medical AI is positively associated with factors such as performance expectancy, habit, hedonic motivation, and trust. Therefore, future medical education should prioritize promoting students' performance in training, and courses should be designed to be both easy to learn and engaging, ensuring that students are equipped with the necessary skills to succeed in their future medical careers.
80The impact of ChatGPT training in the nursing process on nursing students' problem-solving skills, attitudes towards artificial intelligence, competency, and satisfaction levels: Single-blind randomized controlled study.PubMed
Burcu Arkan, Öznur Erbay Dallı, Belgin Varol
Nurse Educ Today. 2025 Sep;152:106765. doi: 10.1016/j.nedt.2025.106765. Epub 2025 May 3.
AIM: This study aimed to evaluate the impact of training on the use of ChatGPT in the nursing process on nursing undergraduate students' problem-solving skills, attitudes towards artificial intelligence, competencies related to the nursing process, and satisfaction levels. DESIGN: Single blind randomized controlled study. METHODS: The sample of the study consisted of 48 experimental and 48 control groups, totaling 96 nursing students. The intervention group received the "Artificial Intelligence Integrated Nursing Process" training program. The control group only received the standard nursing process education. The data collection instruments were administered before the training and after the training. Chi-square analysis was used to compare variables, independent samples t-tests were used for comparisons between groups, and paired t-tests were used for within-group comparisons. RESULTS: In the post-education measurements, students in the intervention group demonstrated a significantly lower average total problem solving inventory score compared to those in the control group, indicating an improvement in problem-solving skills. The intervention group showed an increase in positive attitudes towards artificial intelligence compared to the control group and a decrease in negative attitudes. In the intervention group, students showed a significant increase in their nursing process competency levels and satisfaction levels after the training, whereas no significant change was observed in the control group. CONCLUSION: This study demonstrates that the training provided on the use of ChatGPT in the nursing process positively impacted nursing students' problem-solving skills, attitudes towards artificial intelligence, competencies related to the nursing process, and levels of satisfaction.