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

paperactiveprovisionalevaluating-multi-turn-multimodal-diagnostic-reasoning-on-challenging-real-world-clinical-cases-6f674a53·1 events·first seen 47h ago

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

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ClinMM-Bench

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6arXiv · cs.CL·47h ago·source ↗

ClinMM-Bench: Multi-turn multimodal benchmark for clinical diagnostic reasoning across 1,089 real-world cases

Researchers introduce ClinMM-Bench, a benchmark of 1,089 real-world clinical cases and 3,760 medical images spanning eight specialties, designed to evaluate multimodal LLMs on multi-turn diagnostic reasoning with progressive information disclosure. Fifteen MLLMs were evaluated using a two-level framework assessing both diagnostic accuracy and reasoning quality. Proprietary models led on accuracy but all models showed limited rates of fully correct diagnoses, and error analysis identified five failure modes including visual hallucination and premature closure. The work addresses a gap in clinical AI evaluation by moving beyond single-turn isolated tasks toward more realistic diagnostic workflows.

Evaluation and Benchmarking Multimodal Progress Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases ClinMM-Bench