Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis

The aim is to assess GPT-4V's (OpenAI) diagnostic accuracy and its capability to identify glaucoma-related features compared to expert evaluations. Evaluation of multimodal large language models for reviewing fundus images in glaucoma. A total of 300 fundus images from 3 public datasets (ACRIMA...

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Veröffentlicht in:	Ophthalmology science (Online) Jg. 5; H. 2; S. 100667
Hauptverfasser:	Jalili, Jalil, Jiravarnsirikul, Anuwat, Bowd, Christopher, Chuter, Benton, Belghith, Akram, Goldbaum, Michael H., Baxter, Sally L., Weinreb, Robert N., Zangwill, Linda M., Christopher, Mark
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier Inc 01.03.2025 Elsevier
Schlagworte:	Artificial intelligence Fundus image analysis Glaucoma detection GPT-4V Large multimodal models Ophthalmology Original CDR Fundus image analysis GPT-4V MM-LLM AI UCSD API GPT Large multimodal models LLM Artificial intelligence Glaucoma detection large language model application programmer interface generative pretrained transformer cup-to-disc ratio multimodal large language model University of California, San Diego
ISSN:	2666-9145, 2666-9145
Online-Zugang:	Volltext
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