卫生职业教育中的自然语言处理：一项范围综述
Natural Language Processing in Health Professions Education: A Scoping Review
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中文总结 AI 辅助
该范围综述遵循特定框架和指南，综合64项研究，梳理NLP和AI在卫生职业教育中的应用，确定七个主题领域，发现有技术前景但也有挑战，强调公共卫生教育是证据空白区及未来研究投资重点。
中文摘要 AI 辅助
自然语言处理（NLP）和人工智能（AI）正在迅速改变卫生职业教育，但尚无范围综述系统地梳理它们在包括公共卫生在内的全谱健康教育背景中的应用。本综述遵循阿克西和奥马利框架及PRISMA-ScR指南，综合了2015年至2026年发表的64项研究的证据，这些研究通过检索多个数据库识别得出。确定了七个主题领域，发现有显著技术前景，但也存在如幻觉、准确性、算法偏差、数据隐私等挑战。只有四项研究明确涉及公共卫生教育，存在重大证据空白。本综述为NLP和AI在卫生职业教育中的循证整合提供了基础，并突出公共卫生教育是未来研究和投资的优先领域。
英文摘要
Natural language processing (NLP) and artificial intelligence (AI) are rapidly transforming health professions education, yet no scoping review has systematically mapped their applications across the full spectrum of health education contexts, including public health. Following the Arksey and O'Malley framework and PRISMA-ScR guidelines, this review synthesized evidence from 64 studies published between 2015 and 2026, identified through searches of PubMed, ERIC, IEEE Xplore, and Google Scholar. Seven thematic domains were identified: automated assessment, large language models (LLMs) as student-facing learning support, virtual patients and clinical simulation, curriculum analysis and program evaluation, personalized and adaptive learning, public health and health promotion education, and educator and institutional integration. Findings reveal significant technical promise, particularly in automated assessment, clinical simulation, and curriculum analysis, alongside persistent challenges including hallucination and accuracy concerns, algorithmic bias, data privacy, digital divide, overreliance, and absence of standardized outcome measures. Only four studies explicitly addressed public health education, representing a major evidence gap. This review provides a foundation for evidence-informed integration of NLP and AI across health professions education and highlights public health education as a priority area for future research and investment.
