Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases

Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practice, including progressive disclosure of multimodal information, dynamic updating of diagnostic hypotheses, and continuous refinement of clinical reasoning. However, existing evaluations of multimodal large language models (MLLMs) typically rely on single-turn or isolated tasks, making it difficult to fully capture the complexity of real-world clinic

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ethics.ai (28 July 2026), “Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases,” evidence record 14423, https://ethics.ai/record/14423 (originally published by arXiv cs.AI).

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