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

Formative Evaluation of Digital Health Interventions (4926) Ethics, Privacy, and Legal Issues (787) Machine Learning (3053) Chatbots and Conversational Agents (1135) Artificial Intelligence (4542) Generative Language Models Including ChatGPT (1419) Responsible Health AI (194)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76896, first published .
Medical otoscope resting on a laptop keyboard, ONH label visible

Evaluating Locally Run Large Language Models (Gemma 2, Mistral Nemo, and Llama 3) for Outpatient Otorhinolaryngology Care: Retrospective Study

Evaluating Locally Run Large Language Models (Gemma 2, Mistral Nemo, and Llama 3) for Outpatient Otorhinolaryngology Care: Retrospective Study

Authors of this article:

Christoph Raphael Buhr1, 2 Author Orcid Image ;   Christopher Seifen1 Author Orcid Image ;   Katharina Bahr-Hamm1 Author Orcid Image ;   Tilman Huppertz1 Author Orcid Image ;   Johannes Pordzik1 Author Orcid Image ;   Harry Smith3 Author Orcid Image ;   Tom Kelsey3 Author Orcid Image ;   Andrew Blaikie2 Author Orcid Image ;   Christoph Matthias1 Author Orcid Image ;   Sebastian Kuhn4 Author Orcid Image ;   Jonas Eckrich1 Author Orcid Image
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Journals

  1. Ahmadov N, Muradova A, Aliyeva A. “MELMA” in otolaryngology: Medical evaluation of large language model answers. Clinician-rated scoring (MELMA-Q) and web-based auditing (MELMA-W) novel tools for AI assessment. European Archives of Oto-Rhino-Laryngology 2026 View