MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning
This addresses the problem of inadequate clinical reasoning evaluation in AI for medical diagnosis by providing a benchmark that mimics real-world multi-image workflows, though it is incremental as it builds on existing VQA benchmarks.
The authors tackled the lack of multi-image reasoning benchmarks in medical VQA by introducing MedFrameQA, a dataset with 2,851 VQA pairs from 9,237 frames, and found that ten advanced multimodal LLMs performed poorly, with most accuracies below 50% and issues like ignoring findings and mis-aggregating evidence.
Existing medical VQA benchmarks mostly focus on single-image analysis, yet clinicians almost always compare a series of images before reaching a diagnosis. To better approximate this workflow, we introduce MedFrameQA -- the first benchmark that explicitly evaluates multi-image reasoning in medical VQA. To build MedFrameQA both at scale and in high-quality, we develop 1) an automated pipeline that extracts temporally coherent frames from medical videos and constructs VQA items whose content evolves logically across images, and 2) a multiple-stage filtering strategy, including model-based and manual review, to preserve data clarity, difficulty, and medical relevance. The resulting dataset comprises 2,851 VQA pairs (gathered from 9,237 high-quality frames in 3,420 videos), covering nine human body systems and 43 organs; every question is accompanied by two to five images. We comprehensively benchmark ten advanced Multimodal LLMs -- both proprietary and open source, with and without explicit reasoning modules -- on MedFrameQA. The evaluation challengingly reveals that all models perform poorly, with most accuracies below 50%, and accuracy fluctuates as the number of images per question increases. Error analysis further shows that models frequently ignore salient findings, mis-aggregate evidence across images, and propagate early mistakes through their reasoning chains; results also vary substantially across body systems, organs, and modalities. We hope this work can catalyze research on clinically grounded, multi-image reasoning and accelerate progress toward more capable diagnostic AI systems.