ArticleTherapeutic advances in ophthalmology
Multimodal LLMs for retinal disease diagnosis via OCT: few-shot versus single-shot learning.
Article in Therapeutic advances in ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating and addressing demographic disparities in medical large language models: a systematic review.International journal for equity in health · 2025Pooled it
- Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Performance of 4 artificial intelligence chatbots in responding to multiple choice questions in operative dentistry.BMC oral health · 2026Article
- Multimodal Deep Learning for Diabetic Retinopathy: A Survey.Journal of ophthalmology · 2026Review
- Comparative performance of contemporary multimodal large language models in retinal imaging question answering.Frontiers in medicine · 2026Article
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- Performance of Large Language Models and Top-Decile Doctors on an Undergraduate Ophthalmology Examination.Cureus · 2025Article
- Effectiveness of the GPT-4o Model in Interpreting Electrocardiogram Images for Cardiac Diagnostics: Diagnostic Accuracy Study.JMIR AI · 2025Article
- Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support.Communications medicine · 2025Article
- Visual-textual integration in LLMs for medical diagnosis: A preliminary quantitative analysis.Computational and structural biotechnology journal · 2025Article
- Beyond snapshots: the imperative for temporal and contextual integration in multimodal LLMs for retinal diagnostics.Therapeutic advances in ophthalmologyArticle
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Authors and funding
6 authors.
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Abstract
Background and aim: Multimodal large language models (LLMs) have shown potential in processing both text and image data for clinical applications. This study evaluated their diagnostic performance in identifying retinal diseases from optical coherence tomography (OCT) images. Methods: We assessed the diagnostic accuracy of GPT-4o and Claude Sonnet 3.5 using two public OCT datasets (OCTID, OCTDL) containing expert-labeled images of four pathological conditions and normal retinas. Both models were tested using single-shot and few-shot prompts, with an overall of 3088 models' API calls. Statistical analyses were performed to evaluate differences in overall and condition-specific performance. Results: GPT-4o's accuracy improved from 56.29% with single-shot prompts to 73.08% with few-shot prompts ( Conclusion: Few-shot prompted multimodal LLMs show promise for clinical integration, particularly in identifying normal retinas, which could help streamline referral processes in primary care. While these models fall short of the diagnostic accuracy reported in established deep learning literature, they offer simple, effective tools for assisting in routine retinal disease diagnosis. Future research should focus on further validation and integrating clinical text data with imaging.
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