人工智能,扩展现实,以及医学教育中新兴的AI-XR集成
Talia Tene1, Diego Fabián Vique López2, Marlene Jacqueline García Veloz3
1Department of Chemistry, Universidad Técnica Particular de Loja, Loja, Ecuador.
Frontiers in digital health
|January 26, 2026
概括
人工智能 (AI) 和扩展现实 (XR) 在医疗专业教育中表现有前途,改善学习成果. 然而,需要对综合AI-XR干预及其实施挑战进行更多的研究.
科学领域:
- 健康 专业 教育 卫生 专业 教育
- 教育技术的教育技术
- 人工智能 (AI) 是一种人工智能.
- 扩展现实 (XR)
背景情况:
- 人工智能和XR越来越多地用于卫生专业的教育.
- 人工智能,XR及其在集成AI-XR生态系统中的联合使用对教育的影响尚未完全理解.
研究的目的:
- 综合关于教育成果的经验证据和对人工智能,XR和联合AI-XR干预措施在卫生专业教育中的实施考虑.
主要方法:
- 在Scopus,PubMed和IEEE Xplore中按照PRISMA和PICO指南进行了系统的文献搜索.
- 在查后,包括了2019年至2024年间发表的13项经验研究.
- 关于学习者群体,临床领域,AI/XR模式,比较器,结果和实施因素的数据被提取并以叙事方式合成.
主要成果:
- 研究涉及本科和研究生学习者在程序培训,临床决策和沟通技能的研究.
- 大多数研究单独评估了AI或XR;很少有人在单一干预中整合了两者.
- 与传统教学相比,干预措施通常报告了知识,技能,准确性或参与度的提高,接受度很高. 常见的挑战包括成本,技术可靠性,可用性,教师准备,数字素养和数据隐私.
结论:
- 目前的证据表明,人工智能,XR和AI-XR集成对健康专业教育中的学习和绩效具有初步好处.
- 集成AI-XR干预的数量有限和方法上的弱点限制了研究结果的确定性和通用性.
- 未来的研究需要严格的设计,比较AI-only,XR-only和AI-XR方法,以及教师发展和技术支持.
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