Sana Loubbairi, Yasmine El Moussaoui, Laila Lahlou, Imad Chakri, Hicham Nassik
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引用次数: 0
Abstract
Purpose: Artificial intelligence (AI)-driven simulation is an emerging approach in healthcare education that enhances learning effectiveness. This review examined its impact on the development of non-technical skills among medical learners.
Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic review was conducted using the following databases: Web of Science, ScienceDirect, Scopus, and PubMed. The quality of the included studies was assessed using the Mixed.
Methods: Appraisal Tool. The protocol was previously registered in PROSPERO (CRD420251038024).
Results: Of the 1,442 studies identified in the initial search, 20 met the inclusion criteria, involving 2,535 participants. The simulators varied considerably, ranging from platforms built on symbolic AI methods to social robots powered by computational AI. Among the 15 AI-driven simulators, 10 used ChatGPT or its variants as virtual patients. Several studies evaluated multiple non-technical skills simultaneously. Communication and clinical reasoning were the most frequently assessed skills, appearing in 12 and 6 studies, respectively, which generally reported positive outcomes. Improvements were also noted in decision-making, empathy, self-confidence, critical thinking, and problem-solving. In contrast, emotional regulation, assessed in a single study, showed no significant difference. Notably, none of the studies examined reflection, reflective practice, teamwork, or leadership.
Conclusion: AI-driven simulation shows substantial potential for enhancing non-technical skills in medical education, particularly communication and clinical reasoning. However, its effects on several other non-technical skills remain unclear. Given heterogeneity in study designs and outcome measures, these findings should be interpreted cautiously. These considerations highlight the need for further research to support integrating this innovative approach into medical curricula.
目的：人工智能（AI）驱动的模拟是一种新兴的医疗保健教育方法，可以提高学习效率。本综述探讨了其对医学学习者非技术技能发展的影响。方法：遵循PRISMA（系统评价和荟萃分析的首选报告项目）指南，使用以下数据库进行系统评价：Web of Science、ScienceDirect、Scopus和PubMed。纳入研究的质量用Mixed进行评估。方法：评价工具。该协议先前在PROSPERO （CRD420251038024）中注册。结果：在最初的检索中确定的1442项研究中，有20项符合纳入标准，涉及2,535名参与者。模拟器变化很大，从基于符号人工智能方法的平台到由计算人工智能驱动的社交机器人。在15个人工智能模拟器中，有10个使用ChatGPT或其变体作为虚拟患者。一些研究同时评估了多种非技术技能。沟通和临床推理是最常被评估的技能，分别出现在12项和6项研究中，这些研究通常报告了积极的结果。在决策、同理心、自信、批判性思维和解决问题方面也有所改善。相比之下，在一项研究中评估的情绪调节没有显示出显著差异。值得注意的是，这些研究都没有考察反思、反思实践、团队合作或领导力。结论：人工智能驱动的模拟显示出增强医学教育非技术技能的巨大潜力，特别是沟通和临床推理。然而，它对其他一些非技术技能的影响尚不清楚。考虑到研究设计和结果测量的异质性，这些发现应谨慎解释。这些考虑突出表明需要进一步研究以支持将这一创新方法纳入医学课程。
期刊介绍：
Journal of Educational Evaluation for Health Professions aims to provide readers the state-of-the art practical information on the educational evaluation for health professions so that to increase the quality of undergraduate, graduate, and continuing education. It is specialized in educational evaluation including adoption of measurement theory to medical health education, promotion of high stakes examination such as national licensing examinations, improvement of nationwide or international programs of education, computer-based testing, computerized adaptive testing, and medical health regulatory bodies. Its field comprises a variety of professions that address public medical health as following but not limited to: Care workers Dental hygienists Dental technicians Dentists Dietitians Emergency medical technicians Health educators Medical record technicians Medical technologists Midwives Nurses Nursing aides Occupational therapists Opticians Oriental medical doctors Oriental medicine dispensers Oriental pharmacists Pharmacists Physical therapists Physicians Prosthetists and Orthotists Radiological technologists Rehabilitation counselor Sanitary technicians Speech-language therapists.
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