Integrating Chain‐of‐Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis From Clinical Notes

Abstract

Background: Several studies show that large language models (LLMs) struggle with phenotype‐driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models such as GPT and LLaMA to predict candidate genes. However, in real‐world settings, foundation models are not optimized for domain‐specific tasks such as clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG‐driven CoT and CoT‐driven RAG, two methods that combine Chain‐of‐Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five‐question CoT protocol mimics expert reasoning, whereas RAG retrieves data from sources such as HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5980 Phenopacket‐derived notes, 255 literature‐based narratives, and two cohorts of 1088 noisy clinical notes from a children's hospital. Results: We found that recent foundation models, including Llama 3.3–70B‐Instruct and DeepSeek‐R1‐Distill‐Llama‐70B, outperformed earlier versions such as Llama 2 and GPT‐3.5. We also showed that RAG‐driven CoT and CoT‐driven RAG both outperform foundation models in candidate gene prioritization from clinical notes; in particular, both methods with DeepSeek backbone resulted in a top‐10 gene accuracy of over 40% on Phenopacket‐derived clinical notes, although performance was lower on noisier hospital clinical notes. RAG‐driven CoT works better for high‐quality notes, where early retrieval can anchor the subsequent reasoning steps in domain‐specific evidence, whereas CoT‐driven RAG has advantage when processing lengthy and noisy notes. Conclusions: Integrating CoT and RAG enhances LLMs' understanding of clinical notes in the context of rare disease diagnosis, and can facilitate various downstream medical tasks.