Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning

• URL: http://arxiv.org/abs/2512.00818v1
• Date: Sun, 30 Nov 2025 09:56:50 GMT
• Title: Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning
• Authors: Haozhen Gong, Xiaozhong Ji, Yuansen Liu, Wenbin Wu, Xiaoxiao Yan, Jingjing Liu, Kai Wu, Jiazhen Pan, Bailiang Jian, Jiangning Zhang, Xiaobin Hu, Hongwei Bran Li,
• Abstract summary: We present Med-CMR, a fine-grained Medical Complex Multimodal benchmark.<n>Med-CMR distinguishes from existing counterparts by three core features.<n>We evaluate 18 state-of-the-art MLLMs with Med-CMR, revealing GPT-5 as the top-performing commercial model.
• Score: 37.6854362777847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: MLLMs MLLMs are beginning to appear in clinical workflows, but their ability to perform complex medical reasoning remains unclear. We present Med-CMR, a fine-grained Medical Complex Multimodal Reasoning benchmark. Med-CMR distinguishes from existing counterparts by three core features: 1) Systematic capability decomposition, splitting medical multimodal reasoning into fine-grained visual understanding and multi-step reasoning to enable targeted evaluation; 2) Challenging task design, with visual understanding across three key dimensions (small-object detection, fine-detail discrimination, spatial understanding) and reasoning covering four clinically relevant scenarios (temporal prediction, causal reasoning, long-tail generalization, multi-source integration); 3) Broad, high-quality data coverage, comprising 20,653 Visual Question Answering (VQA) pairs spanning 11 organ systems and 12 imaging modalities, validated via a rigorous two-stage (human expert + model-assisted) review to ensure clinical authenticity. We evaluate 18 state-of-the-art MLLMs with Med-CMR, revealing GPT-5 as the top-performing commercial model: 57.81 accuracy on multiple-choice questions (MCQs) and a 48.70 open-ended score, outperforming Gemini 2.5 Pro (49.87 MCQ accuracy, 45.98 open-ended score) and leading open-source model Qwen3-VL-235B-A22B (49.34 MCQ accuracy, 42.62 open-ended score). However, specialized medical MLLMs do not reliably outperform strong general models, and long-tail generalization emerges as the dominant failure mode. Med-CMR thus provides a stress test for visual-reasoning integration and rare-case robustness in medical MLLMs, and a rigorous yardstick for future clinical systems.

Related papers

• MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement [63.82954136824963]
  Medical Vision-Language Models excel at perception tasks with complex clinical reasoning required in real-world scenarios.<n>We propose a novel reasoning MedVLM that addresses these challenges through domain-specific adaptation and guideline reinforcement.
  arXiv  Detail & Related papers  (2026-01-16T02:32:07Z)
• M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding [66.78251988482222]
  Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models by encouraging step-by-step intermediate reasoning.<n>Current benchmarks for medical image understanding generally focus on the final answer while ignoring the reasoning path.<n>M3CoTBench aims to foster the development of transparent, trustworthy, and diagnostically accurate AI systems for healthcare.
  arXiv  Detail & Related papers  (2026-01-13T17:42:27Z)
• OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks [41.33747208780257]
  multimodal large language models (MLLMs) are increasingly assisting in brain imaging analysis.<n>Current brain-oriented visual question-answering (VQA) benchmarks either cover a few imaging modalities or are limited to coarse-grained pathological descriptions.<n>We introduce OmniBrainBench, the first comprehensive multimodal VQA benchmark designed to assess the multimodal comprehension capabilities of MLLMs in brain imaging analysis.
  arXiv  Detail & Related papers  (2025-11-02T08:11:55Z)
• Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models [57.73472878679636]
  We introduce Med-RewardBench, the first benchmark specifically designed to evaluate medical reward models and judges.<n>Med-RewardBench features a multimodal dataset spanning 13 organ systems and 8 clinical departments, with 1,026 expert-annotated cases.<n>A rigorous three-step process ensures high-quality evaluation data across six clinically critical dimensions.
  arXiv  Detail & Related papers  (2025-08-29T08:58:39Z)
• MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering [4.285647375182588]
  Large language models (LLMs) have shown promise in medical question answering but often struggle with hallucinations and shallow reasoning.<n>Retrieval-augmented generation (RAG) offers a practical and privacy-preserving way to enhance LLMs with external medical knowledge.<n>We introduce MedCoT-RAG, a domain-specific framework that combines causal-aware document retrieval with structured chain-of-thought prompting.
  arXiv  Detail & Related papers  (2025-08-20T05:43:26Z)
• An Agentic System for Rare Disease Diagnosis with Traceable Reasoning [69.46279475491164]
  We introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM)<n>DeepRare generates ranked diagnostic hypotheses for rare diseases, each accompanied by a transparent chain of reasoning.<n>The system demonstrates exceptional diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013 diseases.
  arXiv  Detail & Related papers  (2025-06-25T13:42:26Z)
• CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making [42.28216499263317]
  We introduce Med-Zero-17K, a curated dataset for pure RL-based training, encompassing over 30 medical image modalities and 24 clinical tasks.<n>We propose a novel large-scale RL framework for Med-VLMs, which integrates rewards to ensure fidelity between perception and reasoning, consistency in reasoning-to-answer, and rule-based accuracy for final responses.
  arXiv  Detail & Related papers  (2025-06-15T13:42:46Z)
• MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility [38.33724495011223]
  We introduce MedOrch, a novel framework that orchestrates specialized tools and reasoning agents to provide comprehensive medical decision support.<n>We evaluate MedOrch across three medical applications: Alzheimer's disease diagnosis, chest X-ray interpretation, and medical visual question answering.
  arXiv  Detail & Related papers  (2025-05-30T21:13:12Z)
• EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis [62.00431604976949]
  EndoBench is the first comprehensive benchmark specifically designed to assess MLLMs across the full spectrum of endoscopic practice.<n>We benchmark 23 state-of-the-art models, including general-purpose, medical-specialized, and proprietary MLLMs.<n>Our experiments reveal: proprietary MLLMs outperform open-source and medical-specialized models overall, but still trail human experts.
  arXiv  Detail & Related papers  (2025-05-29T16:14:34Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.