Large Language Models in Medicine: The Potentials and Pitfalls

Checked 30 Sept 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.

The sources disagree. The gate chose the strictest class (red). A human review set it to yellow: “Applies to the submitted version (preprint) in an open repository (OpenAlex/Unpaywall, oa_status green), which is the copy this system uses; the version of record is under publisher terms (Crossref). Reviewed by Fredrik 2026-09-30. Corrected 2026-10-01: the earlier note called it the accepted manuscript; Unpaywall lists it as submittedVersion.”

Large language models (LLMs) are artificial intelligence models trained on vast text data to generate humanlike outputs. They have been applied to various tasks in health care, ranging from answering medical examination questions to generating clinical reports. With increasing institutional partnerships between companies producing LLMs and health systems, the real-world clinical application of these models is nearing realization. As these models gain traction, health care practitioners must understand what LLMs are, their development, their current and potential applications, and the associated pitfalls in a medical setting. This review, coupled with a tutorial, provides a comprehensive yet accessible overview of these areas with the aim of familiarizing health care professionals with the rapidly changing landscape of LLMs in medicine. Furthermore, the authors highlight active research areas in the field that promise to improve LLMs' usability in health care contexts.

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