ArticleAdvances in ophthalmology practice and research
Benchmarking large multimodal models for ophthalmic visual question answering with OphthalWeChat.
Article in Advances in ophthalmology practice and research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An integrative review on disease pathogenesis, diagnostic techniques, management strategies, and futuristic innovations in ocular malignancies.International ophthalmology · 2026Review
- Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.NPJ digital medicine · 2026Article
- 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
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10 authors.
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Abstract
Purpose: To develop a bilingual multimodal visual question answering (VQA) benchmark for evaluating Vision-language models (VLMs) in ophthalmology. Methods: In this cross-sectional study, ophthalmic image posts and associated captions published between Jan 1, 2016, and Dec 31, 2024, were collected from WeChat Official Accounts. Based on these captions, bilingual question-answer (QA) pairs in Chinese and English were generated using GPT-4o-mini. QA pairs were categorized into six subsets by question type and language: binary (Binary_CN, Binary_EN), single-choice (Single-choice_CN, Single-choice_EN), and open-ended (Open-ended_CN, Open-ended_EN). The benchmark was used to evaluate six VLMs: GPT-4o, Gemini 2.0 Flash, Qwen2.5-VL-72B-Instruct, Janus-Pro-7B, InternVL3-8B, and HealthGPT-L14. Primary outcome was overall accuracy; secondary outcomes included subset-, subspeciality-, and modality-specific accuracy. Performance on open-ended questions were also quantified using language-based metrics, including AlignScore, BARTScore, BERTScore, BLEU, CIDEr, METEOR, and ROUGE_L. Error types in open-ended responses were manually analyzed through stratified sampling. Results: OphthalWeChat included 3469 images and 30120 QA pairs cover 9 ophthalmic subspecialties, 548 conditions, 29 imaging modalities, and 68 modality combinations. Gemini 2.0 Flash achieved the highest overall accuracy (0.555), significantly outperforming GPT-4o (0.527), Qwen2.5-VL-72B-Instruct (0.520), HealthGPT-L14 (0.502), InternVL3-L14 (0.453), and Janus-Pro-7B (0.333) (all Conclusions: This study presents the first bilingual VQA benchmark for ophthalmology, distinguished by its real-world context and inclusion of multiple examinations per patient. The dataset enables quantitative evaluation of VLMs, supporting the development of accurate and specialized AI systems for eye care.
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