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This paper is in the following e-collection/theme issue:

Generative Language Models Including ChatGPT (893) Clinical Informatics (1502) Clinical Information and Decision Making (2154) Information Retrieval (367) Natural Language Processing (997) Electronic Health Records (1424) Ontologies, Classifications, and Coding (358) Infectious Diseases (non-STD/STI) (1770) AI Language Models in Health Care (292)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72984, first published .
Doctor using laptop with medical icons and data visualization

Large Language Model Symptom Identification From Clinical Text: Multicenter Study

Large Language Model Symptom Identification From Clinical Text: Multicenter Study

Authors of this article:

Andrew J McMurry1, 2 Author Orcid Image ;   Dylan Phelan1 Author Orcid Image ;   Brian E Dixon3, 4 Author Orcid Image ;   Alon Geva1, 5 Author Orcid Image ;   Daniel Gottlieb1, 6 Author Orcid Image ;   James R Jones1 Author Orcid Image ;   Michael Terry1 Author Orcid Image ;   David E Taylor4 Author Orcid Image ;   Hannah Callaway4 Author Orcid Image ;   Sneha Manoharan4 Author Orcid Image ;   Timothy Miller1, 2 Author Orcid Image ;   Karen L Olson1, 2 Author Orcid Image ;   Kenneth D Mandl1, 2 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

Journals

  1. Öğdü Ç, Arslanoğlu K, Karaköse M. An Adaptive Multi-Agent LLM-Based Clinical Decision Support System Integrating Biomedical RAG and Web Intelligence. IEEE Access 2025;13:167390 View
  2. Wen P, Ai Q, Fan X, Gu Z, Chen B, Qian H. Programmable Smart CAR-T design: A new paradigm in precision immunotherapy driven by logic gates, conditional activation, allogeneic strategies, and artificial intelligence. Cancer Letters 2026;640:218257 View
  3. Bode L, Xu R, Garber M, Mandl K, Mcmurry A. Identifying Undiagnosed High-Risk Suicidality Cases by Matching Patients With a Similar Comorbidity Burden: Retrospective Observational Study. JMIR Formative Research 2026;10:e81499 View
  4. Kim C, Seo J, Kim C, Kim W, Kim S, Ko E, Kim J. SymptoMatch: Improving Clinical Symptom Recognition via Bidirectional Learning From Layman Expression. IEEE Access 2026;14:53378 View
  5. Schipper A, Belgers P, O'Connor R, van de Wouw L, Builtjes L, Bosma J, Kusters R, Kurstjens S, Rutten M, van Ginneken B. Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study. JMIR Medical Informatics 2026;14:e81500 View