ClinMM-Bench evaluates multi-turn clinical diagnostic reasoning across 15 multimodal large models.

Yang et al. propose ClinMM-Bench, which uses 1,089 challenging cases from published case reports and 3,760 medical images to evaluate 15 multimodal large models on clinical diagnostic reasoning under multi-turn progressive information disclosure. The benchmark covers eight specialties: dermatology, emergency medicine, internal medicine, nephrology, neurology, oncology, ophthalmology, and radiology, with an average of 5.45 turns of dialogue disclosing information, scored by GPT-5-medium and Claude-4.5-Sonnet as independent reviewers on a 0 to 2 scale. The authors categorize failures into five types: information synthesis failure, knowledge mapping errors, perception errors, premature conclusions, and visual hallucinations, and find that medical specialization yields no benefit at the 27B scale, extended reasoning traces do not stably improve accuracy and often reduce factual recall, with neurology (0.949) and ophthalmology (0.931) scoring highest, and internal medicine (0.665) and radiology (0.646) being the most difficult. The authors argue that current models lack the reasoning faithfulness required for autonomous clinical use, and accuracy alone is insufficient as a safety metric for medical AI.