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数字化巴林特:在反思性实践中运用人工智能

The digital Balint: using AI in reflective practice.

作者信息

Lewis Marcus, Hayhoe Benedict

机构信息

Abbey Medical Centre, London, UK.

Royal Free Hospital GP Speciality Training Scheme, Royal Free Hospital, London, UK.

出版信息

Educ Prim Care. 2024 Nov;35(6):198-202. doi: 10.1080/14739879.2024.2372606. Epub 2024 Aug 23.

Abstract

Reflective practice is fundamental to postgraduate general practitioner (GP) training and ongoing professional development. However, real-world challenges like time constraints and professional isolation often limit meaningful engagement with this critical skill. This article proposes that large language models (LLMs), sophisticated artificial intelligence systems, may have potential for enhancing reflective practice. We present three case studies, in which we explore the ability of LLMs to generate thought-provoking questions, which could prompt GPs to consider new angles, address underlying factors, and bridge the gap between theory and practice. Our findings suggest that LLMs could help reframe experiences and foster deeper self reflection, particularly for isolated practitioners. While ethical concerns regarding privacy, over reliance, and potential biases exist, we consider the possibility of responsibly integrating LLMs into reflective practice. For trainees, AI-generated questions might complement personal reflection under guidance. For GPs working in isolation, LLMs present an opportunity to enhance reflective practice, challenging us to consider a place for this technological innovation without diminishing the human aspects essential to medical practice.

摘要

反思性实践是研究生全科医生(GP)培训及持续专业发展的基础。然而,诸如时间限制和职业孤立等现实世界的挑战常常限制了对这一关键技能的有效运用。本文提出,大型语言模型(LLMs)这种复杂的人工智能系统,可能具有增强反思性实践的潜力。我们展示了三个案例研究,在其中探究大型语言模型生成发人深省问题的能力,这些问题可以促使全科医生考虑新的角度、解决潜在因素,并弥合理论与实践之间的差距。我们的研究结果表明,大型语言模型有助于重新构建经验并促进更深入的自我反思,特别是对于孤立的从业者。虽然存在关于隐私、过度依赖和潜在偏见的伦理问题,但我们考虑了将大型语言模型负责任地整合到反思性实践中的可能性。对于受训人员而言,人工智能生成的问题可能会在指导下补充个人反思。对于孤立工作的全科医生来说,大型语言模型提供了增强反思性实践的机会,促使我们思考在不削弱医疗实践中至关重要的人文因素的前提下,为这项技术创新留出一席之地。

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