Barriers to breakthroughs: A scoping review of generative AI in healthcare simulation

IF 2.5 3区医学 Q1 NURSING

Clinical Simulation in Nursing Pub Date : 2025-08-28 DOI:10.1016/j.ecns.2025.101791

Erika Janssen PhD, MSN, RN , Rebecca McLagan MS. Ed , Jessica Habeck DNP , Seon Yoon Chung PhD., RN, CHSE , Erin C. McArthur MLIS , Polly Anderson MSN

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引用次数: 0

Abstract

Background

Generative artificial intelligence (AI) is an emerging technology in healthcare education with potential to enhance simulation by addressing logistical barriers and by providing increased access to diverse settings in healthcare education, leading to improved learning outcomes. This rapid scoping review explores the use of generative AI in simulation-based education.

Methods

Searches were conducted in CINAHL, Medline, PsycINFO, ScienceDirect, and Web of Science using terms such as “generative artificial intelligence” and “healthcare simulation.” The review followed the World Health Organization (WHO) Rapid Review Guide and was structured using Arksey and O'Malley's five-stage framework for scoping reviews.

Results

After applying inclusion and exclusion criteria, 15 articles were included. Five themes emerged: (1) removal of logistical barriers, (2) authentic practice, (3) distinctive value, (4) limitations of generative AI, and (5) potential with human oversight. Generative AI improves access to simulation by creating cost-effective, scalable, and realistic scenarios while fostering critical thinking through reflective learning. However, challenges such as misinformation and ethical concerns remain.

Conclusions

This scoping review identified growing momentum around generative AI's role in healthcare simulation. While early studies highlight its potential to support scalable, adaptive, and authentic training experiences, effective integration requires strong governance, ethical safeguards, and human oversight.

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突破的障碍：医疗保健模拟中生成人工智能的范围审查

生成式人工智能（AI）是医疗保健教育领域的一项新兴技术，有可能通过解决后勤障碍和提供更多的医疗保健教育环境来增强模拟，从而改善学习成果。这篇快速的范围审查探讨了生成式人工智能在基于模拟的教育中的应用。方法在CINAHL、Medline、PsycINFO、ScienceDirect和Web of Science中使用“生成式人工智能”和“医疗保健模拟”等术语进行搜索。该审查遵循了世界卫生组织（世卫组织）快速审查指南，并采用了Arksey和O'Malley的五阶段范围审查框架。结果应用纳入和排除标准，纳入文献15篇。出现了五个主题：(1)消除物流障碍，(2)真实的实践，(3)独特的价值，(4)生成式人工智能的局限性，以及(5)人类监督的潜力。生成式人工智能通过创建具有成本效益、可扩展和现实的场景来改善对模拟的访问，同时通过反思性学习培养批判性思维。然而，诸如错误信息和道德问题等挑战仍然存在。本综述确定了围绕生成式人工智能在医疗保健模拟中的作用的增长势头。虽然早期的研究强调了其支持可扩展、自适应和真实的培训经验的潜力，但有效的集成需要强有力的治理、道德保障和人力监督。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Simulation in Nursing NURSING-

CiteScore

5.50

自引率

15.40%

发文量

107

期刊介绍： Clinical Simulation in Nursing is an international, peer reviewed journal published online monthly. Clinical Simulation in Nursing is the official journal of the International Nursing Association for Clinical Simulation & Learning (INACSL) and reflects its mission to advance the science of healthcare simulation. We will review and accept articles from other health provider disciplines, if they are determined to be of interest to our readership. The journal accepts manuscripts meeting one or more of the following criteria: Research articles and literature reviews (e.g. systematic, scoping, umbrella, integrative, etc.) about simulation Innovative teaching/learning strategies using simulation Articles updating guidelines, regulations, and legislative policies that impact simulation Leadership for simulation Simulation operations Clinical and academic uses of simulation.