Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review
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12909_2024_Article_6048.pdf
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Author(s)
McCoy, Liam G.
Ci Ng, Faye Y.
Sauer, Christopher M.
Yap Legaspi, Katelyn E.
Jain, Bhav
Gallifant, Jack
McClurkin, Michael
Hammond, Alessandro
Goode, Deirdre
Gichoya, Judy
Date Issued
October 7, 2024
Journal
BMC Medical Education
Publisher
BioMed Central
Citation
McCoy, L.G., Ci Ng, F.Y., Sauer, C.M. et al. Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review. BMC Med Educ 24, 1096 (2024).
Version
Final published version
Abstract
Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We suggest how medical education must evolve to prepare clinicians capable of navigating human-AI systems.
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s12909-024-06048-z
