Medical thinking with multiple images
Large language models perform well on many medical QA benchmarks, but real clinical reasoning often requires integrating evidence across multiple images rather than interpreting a single view. We introduce MedThinkVQA, an expert-annotated benchmark for thinking with multiple images, where models must interpret each image, combine cross-view evidence, and answer diagnostic questions with intermediate supervision and step-level evaluation. The dataset contains 8,067 cases, including 720 test cases, with an average of 6.62 images per case, substantially denser than prior work, whose expert-level benchmarks use at most 1.43 images per case. On the test set, the best closed-source models, Clau...
| Authors | Zonghai Yao, Benlu Wang, Yifan Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 April 2026 |
| Last updated at source | 4 May 2026 |
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