Shishehgar Sara, Murray-Parahi Pauline, Alsharaydeh Ethar, Mills Sarah, Liu Xianliang
School of Nursing and Midwifery, Western Sydney University, Australia.
Faculty of Health, Charles Darwin University, Australia.
Int Nurs Rev. 2025 Jun;72(2):e70045. doi: 10.1111/inr.70045.
BACKGROUND/OBJECTIVE: Artificial intelligence (AI) is embedded in healthcare education and practice. Pre-service training on AI technologies allows health professionals to identify the best use of AI. This systematic review explores health students'/academics' perception of using AI in their practice. The authors aimed to identify any gaps in the health curriculum related to AI training that may need to be addressed.
Medline (EBSCO), Web of Science, CINAHL (EBSCO), ERIC, Google Scholar, and Scopus were searched using key terms including health students, health academics, AI, and higher education. Quantitative and qualitative studies published in the last seven years were reviewed. JBI SUMARI was used to facilitate study selection, data extraction, and quality assessment of included articles. Thematic and descriptive data analyses were used to retrieve data. This systematic review has been registered in PROSPERO (CRD42023448005).
Twelve studies, including seven quantitative and five mixed-method studies, provided novel insights into health students' perceptions of using AI in health education or practice. Quantitative findings reported significant variations in attitudes and literacy levels regarding AI across different disciplines and demographics. Senior students and those with doctoral degrees exhibited more favourable outlooks compared with their less experienced counterparts (p < 0.001). Students intending to pursue careers in research demonstrated greater optimism towards AI adoption than those planning to work in clinical practice (p < 0.001). A review of qualitative data, particularly on nursing discipline, revealed four themes, including limited AI literacy, replacement of health specialties with AI vs. providing support, optimism vs. cautiousness about using AI in practice, and ethical concerns. Only one study explored health academics' experiences with AI in education, highlighting a gap in the current literature. This is while that students consistently agreed that universities are the best setting for learning about AI technologies in healthcare highlighting the need for embedding AI training into the health curricula to prepare future healthcare professionals.
CONCLUSION AND IMPLICATIONS FOR NURSING/HEALTH POLICY: This systematic review recommends embedding AI training in health curriculum, offering direction for health education providers and curriculum developers responsible for preparing next-generation healthcare professionals, particularly nurses. Ethical considerations and the future role of AI in healthcare practice remain central concerns to be addressed in both curriculum development and future research. Further research is required to address the implication and cost-effectiveness of embedding AI training into health curricula.
背景/目的：人工智能（AI）已融入医疗保健教育和实践之中。针对人工智能技术的职前培训能让卫生专业人员确定人工智能的最佳用途。本系统评价探讨了卫生专业学生/学者对在其实践中使用人工智能的看法。作者旨在找出健康课程中与人工智能培训相关的可能需要解决的差距。
使用包括卫生专业学生、卫生专业学者、人工智能和高等教育等关键词，对医学期刊数据库（EBSCO）、科学引文索引、护理学与健康领域数据库（EBSCO）、教育资源信息中心、谷歌学术和Scopus进行检索。对过去七年发表的定量和定性研究进行了综述。使用循证卫生保健国际协作组织的循证卫生保健系统评价软件来促进纳入文章的研究选择、数据提取和质量评估。采用主题和描述性数据分析来检索数据。本系统评价已在国际前瞻性系统评价注册库（注册号：CRD42023448005）中登记。
12项研究，包括7项定量研究和5项混合方法研究，为卫生专业学生对在健康教育或实践中使用人工智能的看法提供了新的见解。定量研究结果表明，不同学科和人口统计学特征的学生对人工智能的态度和知识水平存在显著差异。与经验较少的学生相比，高年级学生和拥有博士学位的学生表现出更积极的态度（p<0.001）。打算从事研究工作的学生对采用人工智能的态度比计划从事临床实践的学生更为乐观（p<0.001）。对定性数据的回顾，特别是关于护理学科的数据，揭示了四个主题，包括人工智能知识有限、人工智能取代卫生专业与提供支持、在实践中使用人工智能的乐观与谨慎态度以及伦理问题。只有一项研究探讨了卫生专业学者在教育中使用人工智能的经历，凸显了当前文献中的一个空白。与此同时，学生们一致认为大学是学习医疗保健领域人工智能技术的最佳场所，这突出表明需要将人工智能培训纳入健康课程，以培养未来的医疗保健专业人员。
结论及对护理/卫生政策的启示：本系统评价建议将人工智能培训纳入健康课程，为负责培养下一代医疗保健专业人员（尤其是护士）的健康教育提供者和课程开发者提供指导。伦理考量以及人工智能在医疗保健实践中的未来作用仍然是课程开发和未来研究中需要解决的核心问题。需要进一步研究以探讨将人工智能培训纳入健康课程的影响和成本效益。
