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

AI Language Models in Health Care (230) Clinical Informatics (1408) Natural Language Processing (943) Innovations in Clinical Trials and Research Data Management (135) Ontologies, Classifications, and Coding (341) mHealth in a Clinical Setting (766) Generative Language Models Including ChatGPT (775) Applications of AI (323)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71252, first published .
Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership Common Data Model Queries: Validation and Evaluation Study

Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership Common Data Model Queries: Validation and Evaluation Study

Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership Common Data Model Queries: Validation and Evaluation Study

Authors of this article:

Kye Hwa Lee1 Author Orcid Image ;   Sujung Jang2 Author Orcid Image ;   Grace Juyun Kim3 Author Orcid Image ;   Sukyoung Park2 Author Orcid Image ;   Doeun Kim2 Author Orcid Image ;   Oh Jin Kwon3 Author Orcid Image ;   Jae-Ho Lee4 Author Orcid Image ;   Young-Hak Kim5 Author Orcid Image
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Journals

  1. Ekingen E, Ucdal M. Performance Comparison of a Neuro-Symbolic Large Language Model System Versus Human Experts in Acute Cholecystitis Management. Journal of Clinical Medicine 2026;15(5):1730 View