MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering
This addresses the need for more reliable and interpretable AI in clinical decision support, though it is incremental as it builds on existing RAG and chain-of-thought methods.
The paper tackled the problem of hallucinations and shallow reasoning in large language models for medical question answering by introducing MedCoT-RAG, a framework that combines causal-aware retrieval with structured chain-of-thought prompting, resulting in performance improvements of up to 10.3% over vanilla RAG and 6.4% over advanced domain-adapted methods on medical benchmarks.
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.