M3CoTBench: New benchmark evaluates LLM reasoning in medical imaging

Researchers from multiple institutions have introduced M3CoTBench, a benchmark specifically designed to evaluate chain-of-thought (CoT) reasoning in multimodal large language models (MLLMs) for medical image understanding. Published on arXiv and accepted at ICLR 2026, the benchmark addresses a critical gap: existing medical image benchmarks focus only on final answers, ignoring the reasoning path that clinicians rely on for diagnostic decisions. M3CoTBench covers 24 examination types (e.g., X-rays, CT scans, MRIs) across 13 tasks of varying difficulty and introduces four CoT-specific evaluation metrics—correctness (accuracy of intermediate steps), efficiency (number of reasoning steps), impact (how much CoT helps final answer), and consistency (reproducibility of reasoning across runs).

Initial testing on multiple MLLMs shows that while CoT improves performance on some tasks, models still frequently produce hallucinated or incomplete reasoning chains, making them unreliable for direct clinical use. The benchmark aims to push the field toward transparent, trustworthy AI that can assist doctors by providing step-by-step diagnostic explanations rather than black-box predictions. By standardizing how CoT reasoning is evaluated in medical imaging, M3CoTBench provides a much-needed tool for researchers to compare models and develop safer AI systems for healthcare.