Work

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

Year: 2025

Type: article

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 language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity. Main Outcome Measures: Accuracy, sensitivity, and specificity of diagnosis. Results: < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608-0.566) and sensitivity (0.618-0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870-0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866. Conclusions: Multimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Source: Ophthalmology Science

DOI: 10.1016/j.xops.2025.100911