Three AI specialists collaborate to diagnose medical images, making fewer mistakes than one expert.
MedCoT introduces a three-tier expert system for medical visual diagnosis that mimics real-world collaborative doctor consultations, enhancing both accuracy and interpretability.
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https://arxiv.org/abs/2412.13736v1
🔍 Original Problem:
→ Current Medical Visual Question Answering systems focus solely on accuracy, neglecting reasoning paths and interpretability crucial for clinical settings. Single-model approaches lack the robustness needed for real-world medical diagnostics.
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🛠️ Solution in this Paper:
→ MedCoT implements a hierarchical expert verification chain with three specialists.
→ Initial Specialist generates preliminary diagnostic rationales from medical images and questions.
→ Follow-up Specialist validates these rationales, retaining effective ones and correcting flawed assessments.
→ Diagnostic Specialist, using sparse Mixture of Experts architecture, processes validated insights to deliver final diagnosis.
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💡 Key Insights:
→ Medical diagnoses require explicit reasoning paths for transparency
→ Multi-expert review systems outperform single-model approaches
→ Sparse Mixture of Experts effectively handles organ-specific diagnoses
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📊 Results:
→ Outperforms 7B parameter LLaVA-Med by 5.52% on VQA-RAD dataset using only 256M parameters
→ Achieves 87.50% accuracy on VQA-RAD and 87.26% on SLAKE-EN datasets
→ Shows 10% improvement in head-related medical queries compared to traditional methods
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