The digital Balint: using AI in reflective practice
File(s)TheDigitalBalint_preprint.pdf (201.04 KB)
Accepted version
Author(s)
Lewis, Marcus
Hayhoe, Benedict
Type
Journal 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.
Date Issued
2024-08-23
Date Acceptance
2024-06-22
Citation
Education for Primary Care, 2024, 35 (6), pp.198-202
ISSN
1367-8523
Publisher
Taylor and Francis Group
Start Page
198
End Page
202
Journal / Book Title
Education for Primary Care
Volume
35
Issue
6
Copyright Statement
Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39178303
Subjects
Artificial intelligence
cognitive reflection
education
general practice
medical
natural language processing
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2024-08-23