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

ABSTRACT:

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

Ultra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large la

SUBMITTER: Most JA 

PROVIDER: S-EPMC12478077 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature