Charow Rebecca, Jeyakumar Tharshini, Younus Sarah, Dolatabadi Elham, Salhia Mohammad, Al-Mouaswas Dalia, Anderson Melanie, Balakumar Sarmini, Clare Megan, Dhalla Azra, Gillan Caitlin, Haghzare Shabnam, Jackson Ethan, Lalani Nadim, Mattson Jane, Peteanu Wanda, Tripp Tim, Waldorf Jacqueline, Williams Spencer, Tavares Walter, Wiljer David
Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
University Health Network, Toronto, ON, Canada.
JMIR Med Educ. 2021 Dec 13;7(4):e31043. doi: 10.2196/31043.
As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education.
With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness.
After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career).
Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective.
This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.
随着人工智能(AI)在医疗保健领域的应用日益增加,让医疗保健专业人员(HCPs)参与人工智能技术的开发、验证和实施变得越来越重要。然而,由于缺乏人工智能素养,大多数医疗保健专业人员并未为这场革命做好充分准备。这是采用和实施人工智能的一个重大障碍,会对患者产生影响。此外,现有的有限人工智能教育项目在医学教育的各个层面都面临着开发和实施的障碍。
为了为未来针对医疗保健专业人员的人工智能教育项目提供参考,本范围综述旨在概述当前或过去的人工智能教育项目的类型,包括这些项目的课程内容、授课方式、教育授课的关键实施因素以及用于评估项目有效性的结果。
在制定搜索策略并进行关键词搜索后,由两名独立评审员进行两阶段筛选过程,以确定研究的 eligibility。当未达成共识时,通过咨询第三位评审员解决冲突。这个过程包括标题和摘要扫描以及全文审查。如果文章讨论了实际的培训项目或教育干预,或潜在的培训项目或教育干预以及拟涵盖的期望内容,聚焦于人工智能,并且是为医疗保健专业人员(在其职业生涯的任何阶段)设计或打算的,则纳入这些文章。
在扫描的10094条独特引用中,确定了41项(0.41%)符合我们纳入标准的研究。在纳入的41项研究中,10项(24%)描述了13个独特的项目,31项(76%)讨论了推荐的课程内容。独特项目的课程内容从人工智能的使用、人工智能的解读以及培养解释人工智能算法得出结果的技能不等。课程主题分为三个主要领域:认知、心理运动和情感。
本综述概述了医学教育中人工智能的当前状况,并强调了医疗保健专业人员有效使用人工智能以提高护理质量和优化患者结果所需的技能和能力。未来的教育工作应侧重于监管策略的制定、课程重新设计的多学科方法、基于能力的课程以及患者与临床医生的互动。