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Cited by 37Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareTopic Modeling

Clinical insights: A comprehensive review of language models in medicine

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Neveditsin, Nikita, Lingras, Pawan, Mago, Vijay·2025·PLOS Digital Health
DOI 10.1371/journal.pdig.0000800PMC PMC12061104
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Abstract

This paper explores the advancements and applications of language models in healthcare, focusing on their clinical use cases. It examines the evolution from early encoder-based systems requiring extensive fine-tuning to state-of-the-art large language and multimodal models capable of integrating text and visual data through in-context learning. The analysis emphasizes locally deployable models, which enhance data privacy and operational autonomy, and their applications in tasks such as text generation, classification, information extraction, and conversational systems. The paper also highlights a structured organization of tasks and a tiered ethical approach, providing a valuable resource for researchers and practitioners, while discussing key challenges related to ethics, evaluation, and implementation.

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  • Although there are many reviews on LLM explainability in general domains, on explainable AI in healthcare, or on LLM applications in medicine, there remain substantial gaps in understanding explainability for LLMs in healthcare.

    “Although there are many reviews on LLM explainability in general domains [18,19], on explainable AI in healthcare [33,34], or on LLM applications in medicine [35-37], we still have substantial gaps in our understanding of explainability for LLMs in healthcare.”

    From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [37]

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Authors
Neveditsin, N., Lingras, P., & Mago, V.
Affiliations
  • Saint Mary's University (CA)
  • York University (CA)
Corresponding author
Mago, Vijay
ORCID
Venue
PLOS Digital Health
Publisher
Public Library of Science (PLoS)
Published
2025-05-08
Volume
4
Issue
5
Pages
e0000800
ISSN
2767-3170 (ISSN-L)
DOI
10.1371/journal.pdig.0000800
PMC
PMC12061104
OpenAlex
W4410203956
Cited by
37
References
218
Citations by year
2026: 25 · 2025: 12
FWCI
2.19
Type
journal article
Language
en
Open access
Licence
Locations
  • PLOS Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
large language models, multimodal models, in-context learning, data privacy, clinical text generation, clinical information extraction, dialogue systems, clinical classification, ethics of AI in healthcare

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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