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MedCoT: Medical Chain of Thought via Hierarchical Expert

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arxiv 2412.13736 v1 pith:PUWHQCK5 submitted 2024-12-18 cs.CV

Jiaxiang Liu , Yuan Wang , Jiawei Du , Joey Tianyi Zhou , Zuozhu Liu This is my paper · ORCID
classification cs.CV
keywords med-vqamedcotexpertinterpretabilitymedicalreasoningspecialistaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings. Besides, current Med-VQA algorithms, typically reliant on singular models, lack the robustness needed for real-world medical diagnostics which usually require collaborative expert evaluation. To address these shortcomings, this paper presents MedCoT, a novel hierarchical expert verification reasoning chain method designed to enhance interpretability and accuracy in biomedical imaging inquiries. MedCoT is predicated on two principles: The necessity for explicit reasoning paths in Med-VQA and the requirement for multi-expert review to formulate accurate conclusions. The methodology involves an Initial Specialist proposing diagnostic rationales, followed by a Follow-up Specialist who validates these rationales, and finally, a consensus is reached through a vote among a sparse Mixture of Experts within the locally deployed Diagnostic Specialist, which then provides the definitive diagnosis. Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches, providing significant improvements in performance and interpretability.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

    cs.AI 2026-07 accept novelty 6.0 of 10

    A dual clinical-computational taxonomy for medical LLM reasoning plus a five-level 5k-sample benchmark showing specialists excel at diagnosis and general models at decision support/dialogue.

  2. 4KAgent: Agentic Any Image to 4K Super-Resolution

    cs.CV 2025-07 reject novelty 6.0 of 10

    An agentic pipeline that plans and executes image restoration from a toolbox of pretrained models to upscale arbitrary images to 4K, reporting state-of-the-art results on many benchmarks.

  3. Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A pure reinforcement learning recipe with mixed rule-based rewards and length control improves Qwen2.5-based models across diverse medical QA formats.

  4. Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical Reasoning

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A new 8-stage chest X-ray VQA benchmark and a context-aware model trained on it.

  5. Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning

    cs.CL 2025-08 conditional novelty 5.0 of 10

    RoMed and CCL: a 144k-question perturbation benchmark for medical VQA and a consistency-plus-contrastive training method that improves LLaVA-Med's accuracy and reduces answer variation.

  6. RARL: Improving Medical VLM Reasoning and Generalization with Reinforcement Learning and LoRA under Data and Hardware Constraints

    cs.CV 2025-06 reject novelty 5.0 of 10

    RARL fine-tunes Qwen2-VL-2B on 716 medical samples with GRPO, LoRA, and a vaguely defined reasoning reward, claiming gains of 7.78% over SFT on reasoning and up to 27% on unseen VQA benchmarks.

  7. Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A "catfish" agent that injects structured dissent into multi-agent LLM teams improves clinical question-answering accuracy by reducing premature consensus.

  8. TAGS: A Test-Time Generalist-Specialist Framework with Retrieval-Augmented Reasoning and Verification

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Without any parameter updates, a retrieval-augmented generalist-specialist agent pair with consistency-based verification raises accuracy on 862 hard medical QA questions for GPT-4o, DeepSeek-R1, and Qwen2.5-7B.

  9. Architecting Clinical Collaboration: Multi-Agent Reasoning Systems for Multimodal Medical VQA

    cs.AI 2025-07 reject novelty 4.0 of 10

    A systematic study on dermatology VQA finds that multi-agent reasoning and retrieval architectures outperform fine-tuned open-source vision-language models, maintaining 70% accuracy under distribution shift.

  10. Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects

    cs.RO 2025-05 conditional novelty 4.0 of 10

    A survey that classifies chain-of-thought methods for autonomous driving into modular, logical, and reflective pipelines, and proposes three evolutionary stages from direct prompting to reinforcement learning.

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