The digital Balint: using AI in reflective practice
Year: 2024
Type: article
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.
Source: Education for Primary Care
Authors: Marcus Lewis, Benedict Hayhoe
Institutions: The Royal Free Hospital, Imperial College London
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W4401834574