A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases

2025-11-18

AI Summary

Rare diseases suffer from prolonged diagnostic timelines and fragmented clinical evidence; moreover, general-purpose large language models exhibit limited clinical reasoning capabilities due to scarce real-world electronic health records (EHRs), outdated medical knowledge, and hallucination. To address these challenges, we propose a domain-specific clinical reasoning paradigm centered on “narrative-first, knowledge-enhanced” inference. Our approach comprises: (1) constructing a physician-validated rare-disease reasoning dataset and domain-specific corpus; (2) designing a knowledge graph–anchored retrieval mechanism and phased chain-of-thought training to integrate non-phenotypic evidence (e.g., imaging, functional tests); and (3) enhancing robustness under noisy conditions and phenotypic overlap via instruction fine-tuning, knowledge graph fusion, and structured reasoning. Evaluated on multicenter real-world EHRs and public benchmarks, our method achieves state-of-the-art performance—matching the diagnostic accuracy of senior clinicians while significantly shortening diagnostic pathways and enabling transparent, auditable clinical decision-making.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domain knowledge, and hallucinations. We assemble a large, domain specialized clinical corpus and a clinician validated reasoning set, and develop RareSeek R1 via staged instruction tuning, chain of thought learning, and graph grounded retrieval. Across multicenter EHR narratives and public benchmarks, RareSeek R1 attains state of the art accuracy, robust generalization, and stability under noisy or overlapping phenotypes. Augmented retrieval yields the largest gains when narratives pair with prioritized variants by resolving ambiguity and aligning candidates to mechanisms. Human studies show performance on par with experienced physicians and consistent gains in assistive use. Notably, transparent reasoning highlights decisive non phenotypic evidence (median 23.1%, such as imaging, interventions, functional tests) underpinning many correct diagnoses. This work advances a narrative first, knowledge integrated reasoning paradigm that shortens the diagnostic odyssey and enables auditable, clinically translatable decision support.

Problem

• Diagnosing rare diseases takes years due to delayed identification
• General medical LLMs struggle with scarce EHR data and inaccuracies
• Conventional methods separate evidence extraction from diagnostic reasoning processes

Innovation

• Staged instruction tuning for clinical reasoning
• Chain of thought learning with graph retrieval
• Augmented retrieval aligning variants to mechanisms