Assessing the Clinical Utility of Multimodal Large Language Models in the Diagnosis and Management of Pigmented Choroidal Lesions.

ABSTRACT:

Purpose

To evaluate the diagnostic and treatment recommendation performance of multimodal large language models (MLLMs) in identifying and classifying retinal lesions as choroidal nevus or melanoma, as well as compare their performance with expert human graders.

Methods

This retrospective cross-sectional study included 48 eyes from 47 patients diagnosed with either choroidal nevus or melanoma. Patient demographics, including age, sex, ethnicity, best-corrected visual acuity (BCVA), and symptoms, were documented. Color fundus, autofluorescence, optical coherence tomography, and B-scan images were collected. The ocular images and patient characteristics were presented to ChatGPT 4.0, Gemini Advanced 1.5 Pro, and Perplexity Pro. Responses were recorded and compared with the cli

SUBMITTER: Mehta NN 

PROVIDER: S-EPMC12530446 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature