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Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation

Objective: To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features. Design: A single-center retrospective study. Subjects: Patients diagnosed with prediabetes and diabetes. Methods: U...

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Autori principali: Jesse A. Most, BA, Evan H. Walker, MS, Nehal N. Mehta, MD, Ines D. Nagel, MD, Jimmy S. Chen, MD, Jonathan F. Russell, MD, PhD, Nathan L. Scott, MD, MPP, Shyamanga Borooah, MBBS, PhD
Natura: Artigo
Lingua:Inglês
Pubblicazione: Elsevier 2026-01-01
Serie:Ophthalmology Science
Soggetti:
Diabetic retinopathy
Ultra-widefield fundus photography
Multimodal large language model
Artificial intelligence
Image analysis
Accesso online:http://www.sciencedirect.com/science/article/pii/S266691452500209X
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