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

#Live Reviewed Article Collection (7) Applications of AI (279) AI Language Models in Health Care (198) Computer-Aided Diagnosis (248) Generative Language Models Including ChatGPT (700)

Published on in Vol 6 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67661, first published .
Rapidly Benchmarking Large Language Models for Diagnosing Comorbid Patients: Comparative Study Leveraging the LLM-as-a-Judge Method

Rapidly Benchmarking Large Language Models for Diagnosing Comorbid Patients: Comparative Study Leveraging the LLM-as-a-Judge Method

Rapidly Benchmarking Large Language Models for Diagnosing Comorbid Patients: Comparative Study Leveraging the LLM-as-a-Judge Method

Authors of this article:

Peter Sarvari1 Author Orcid Image ;   Zaid Al-fagih1 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Sarvari P, Al-fagih Z. Authors’ Response to Peer Reviews of “Rapidly Benchmarking Large Language Models for Diagnosing Comorbid Patients: Comparative Study Leveraging the LLM-as-a-Judge Method”. JMIRx Med 2025;6:e81235 View
  2. Sarvari P, Al-fagih Z, Abou-Chedid A, Jewell P, Taylor R, Imtiaz A. Challenges and Solutions in Applying Large Language Models to Guideline-Based Management Planning and Automated Medical Coding in Health Care: Algorithm Development and Validation. JMIR Biomedical Engineering 2025;10:e66691 View