ArticleScientific reports2025
Integrated visual and text-based analysis of ophthalmology clinical cases using a large language model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in the diagnosis and prognosis of ocular trauma: a systematic review.BMC ophthalmology · 2026Pooled it
- Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Human-AI collaboration using GPT-5 for MR grading of endolymphatic hydrops in Ménière's disease.European radiology · 2026Article
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.Ophthalmology science · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Comparative Performance of Multimodal and Unimodal Large Language Models Versus Multicenter Human Clinical Experts in Aortic Dissection Management.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable neuro-symbolic artificial intelligence for automated interpretation of corneal topography and early keratoconus detection.Frontiers in artificial intelligence · 2026Article
- Comparative performance of contemporary multimodal large language models in retinal imaging question answering.Frontiers in medicine · 2026Article
- Artificial intelligence in fundus photography for type 2 diabetes: a scoping review of systemic biomarkers and multi-organ risk prediction.Frontiers in digital health · 2026Review
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- Comparative analysis of generic vision-language models in detecting and diagnosing inherited retinal diseases using fundus photographs.Eye (London, England) · 2025Article
- Explainable Artificial Intelligence Framework for Predicting Treatment Outcomes in Age-Related Macular Degeneration.Sensors (Basel, Switzerland) · 2025Article
- Diagnostic Performance of ChatGPT in Corneal Disease Recognition From Slit-Lamp Photographs.Cureus · 2025Article
- Review
- An Explainable AI Framework for Corneal Imaging Interpretation and Refractive Surgery Decision Support.Bioengineering (Basel, Switzerland) · 2025Article
- Making Chatbots more human: deep reasoning large language models in ophthalmology.Frontiers in medicine · 2025Review
- The effectiveness of artificial intelligence models in addressing the concerns of families of children with cerebral palsy: a comparative analysis of ChatGPT, Gemini, and DeepSeek.Frontiers in pediatrics · 2025Article
- Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in generative artificial intelligence have enabled analysis of text with visual data, which could have important implications in healthcare. Diagnosis in ophthalmology is often based on a combination of ocular examination, and clinical context. The aim of this study was to evaluate the performance of multimodal GPT-4 (GPT-4 V) in an integrated analysis of ocular images and clinical text. This retrospective study included 40 patients seen in our institution with images of their ocular examinations. Cases were selected by a board-certified ophthalmologist, to represent various pathologies. We provided the model with each patient image, without and then with the clinical context. We also asked two non-ophthalmology physicians to write diagnoses for each image, without and then with the clinical context. Answers for both GPT-4 V and the non-ophthalmologists were evaluated by two board-certified ophthalmologists. Performance accuracies were calculated and compared. GPT-4 V provided the correct diagnosis in 19/40 (47.5%) cases based on images without clinical context, and in 27/40 (67.5%) cases when clinical context was provided. Non-ophthalmologist physicians provided the correct diagnoses in 24/40 (60.0%), and 23/40 (57.5%) of cases without clinical context, and in 29/40 (72.5%) and 27/40 (67.5%) with clinical context. For all study participants adding context improved accuracy (p = 0.033). GPT-4 V is currently able to simultaneously analyze and integrate visual and textual data, and arrive at accurate clinical diagnoses in the majority of cases. Multimodal large language models like GPT-4 V have significant potential to advance both patient care and research in ophthalmology.
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