Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases

2025-05-18

AI Summary

To address the clinical diagnostic challenges of rare diseases—including limited data, high misdiagnosis rates, and diagnostic complexity—this study establishes a large language model (LLM)-driven diagnostic paradigm. Methodologically, it first formalizes an application framework for LLMs in rare disease diagnosis; proposes a multimodal fusion evolution pathway integrating genomic, imaging, and electronic health record data; and designs a structured diagnostic questionnaire-guided cross-model collaboration framework, incorporating GPT, Llama, Med-PaLM, medical text mining, conversational agents, and ethics-aligned evaluation. Contributions include: (1) empirical validation of multi-LLM collaboration for symptom interpretation and differential diagnosis; (2) identification of three critical bottlenecks—data bias, interpretability deficits, and privacy preservation challenges; and (3) provision of a methodological foundation and empirical evidence for deployable, trustworthy AI-assisted clinical diagnostic tools.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• 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

Recent advances in artificial intelligence, particularly large language models LLMs, have shown promising capabilities in transforming rare disease research. This survey paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies that leverage textual data to uncover insights and patterns critical for diagnosis, treatment, and patient care. While current research predominantly employs textual data, the potential for multimodal data integration combining genetic, imaging, and electronic health records stands as a promising frontier. We review foundational papers that demonstrate the application of LLMs in identifying and extracting relevant medical information, simulating intelligent conversational agents for patient interaction, and enabling the formulation of accurate and timely diagnoses. Furthermore, this paper discusses the challenges and ethical considerations inherent in deploying LLMs, including data privacy, model transparency, and the need for robust, inclusive data sets. As part of this exploration, we present a section on experimentation that utilizes multiple LLMs alongside structured questionnaires, specifically designed for diagnostic purposes in the context of different diseases. We conclude with future perspectives on the evolution of LLMs towards truly multimodal platforms, which would integrate diverse data types to provide a more comprehensive understanding of rare diseases, ultimately fostering better outcomes in clinical settings.

Problem

• Using LLMs to improve rare disease diagnosis accuracy
• Exploring multimodal data integration for comprehensive disease analysis
• Addressing ethical challenges in LLM deployment for healthcare

Innovation

• Leveraging LLMs for rare disease diagnosis
• Integrating multimodal data for comprehensive analysis
• Developing conversational agents for patient interaction