Benchmarking Large Multimodal Models for Ophthalmic Visual Question Answering with OphthalWeChat

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 P < 0.001). It also led in both Chinese (0.551) and English subsets (0.559). By subset, Gemini 2.0 Flash excelled in Binary_CN (0.687) and Single-choice_CN (0.666); HealthGPT-L14 performed best in Single-choice_EN (0.739); while GPT-4o ranked highest in Binary_EN (0.717), Open-ended_CN (0.254), and Open-ended_EN (0.271). Language-based metrics showed inconsistent rankings relative to accuracy in open-ended subsets. Performance varied across subspecialties and modalities, with Gemini 2.0 Flash leading in 6 of 9 subspecialties and 11 of top-15 imaging modalities. Error types analysis revealed lesion/diagnosis errors as the most frequent (35.6%–50.6%), followed by anatomical location errors (28.3%–37.5%). 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.