MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering

• URL: http://arxiv.org/abs/2508.15849v1
• Date: Wed, 20 Aug 2025 05:43:26 GMT
• Title: MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering
• Authors: Ziyu Wang, Elahe Khatibi, Amir M. Rahmani,
• Abstract summary: 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.
• Score: 4.285647375182588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have shown promise in medical question answering but often struggle with hallucinations and shallow reasoning, particularly in tasks requiring nuanced clinical understanding. Retrieval-augmented generation (RAG) offers a practical and privacy-preserving way to enhance LLMs with external medical knowledge. However, most existing approaches rely on surface-level semantic retrieval and lack the structured reasoning needed for clinical decision support. We introduce MedCoT-RAG, a domain-specific framework that combines causal-aware document retrieval with structured chain-of-thought prompting tailored to medical workflows. This design enables models to retrieve evidence aligned with diagnostic logic and generate step-by-step causal reasoning reflective of real-world clinical practice. Experiments on three diverse medical QA benchmarks show that MedCoT-RAG outperforms strong baselines by up to 10.3% over vanilla RAG and 6.4% over advanced domain-adapted methods, improving accuracy, interpretability, and consistency in complex medical tasks.

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