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医学教育中的生成式人工智能:对未来职业精神的挑战与可能性的叙述性综述

Generative Artificial Intelligence (AI) in Medical Education: A Narrative Review of the Challenges and Possibilities for Future Professionalism.

作者信息

Komasawa Nobuyasu, Yokohira Masanao

机构信息

Community Medicine Education Promotion Office, Faculty of Medicine, Kagawa University, Miki-cho, JPN.

Department of Medical Education, Kagawa University, Miki-cho, JPN.

出版信息

Cureus. 2025 Jun 18;17(6):e86316. doi: 10.7759/cureus.86316. eCollection 2025 Jun.

Abstract

The rapid emergence of generative artificial intelligence (AI) is reshaping the landscape of medical education and healthcare. Unlike traditional AI, which focuses on classification or prediction, generative AI can create novel content-such as clinical notes, patient education materials, and simulated interactions-based on large-scale data. This capacity offers significant opportunities for personalized learning, clinical efficiency, and patient engagement. However, the integration of generative AI also introduces complex challenges, including ethical ambiguity, misinformation, accountability, data privacy risks, and potential erosion of critical thinking skills. These risks are especially salient in educational settings, where future physicians are still developing their professional identities. In this narrative review, we examine the dual role of generative AI as both a transformative tool and a source of ethical and professional disruption. We analyze its benefits and challenges across educational and clinical domains and argue that the traditional model of medical professionalism must evolve in response. Drawing on international literature and diverse cultural contexts in medical education, we propose a redefined framework for AI-era professionalism-one that integrates technological fluency with enduring humanistic values such as empathy, integrity, and accountability. This review offers AI-integrated medical professionalism to prepare future physicians to use generative AI responsibly, ethically, and in service of patient-centered care.

摘要

生成式人工智能(AI)的迅速崛起正在重塑医学教育和医疗保健的格局。与专注于分类或预测的传统人工智能不同,生成式人工智能可以基于大规模数据创建新颖的内容,如临床记录、患者教育材料和模拟互动。这种能力为个性化学习、临床效率和患者参与提供了重大机遇。然而,生成式人工智能的整合也带来了复杂的挑战,包括伦理模糊性、错误信息、问责制、数据隐私风险以及批判性思维技能的潜在侵蚀。这些风险在教育环境中尤为突出,因为未来的医生仍在塑造他们的职业身份。在这篇叙述性综述中,我们审视了生成式人工智能作为一种变革性工具以及伦理和专业干扰源的双重作用。我们分析了其在教育和临床领域的益处和挑战,并认为传统的医学专业模式必须相应地发展。借鉴国际文献和医学教育中的不同文化背景,我们提出了一个为人工智能时代重新定义的专业框架——一个将技术熟练程度与同理心、正直和问责制等持久人文价值观相结合的框架。这篇综述提供了人工智能整合的医学专业素养,以使未来的医生能够负责任、合乎伦理地使用生成式人工智能,并服务于以患者为中心的医疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ef/12276793/789844e49682/cureus-0017-00000086316-i01.jpg

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