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

TL;DR

This survey reviews how large language models are transforming rare disease diagnosis through textual data analysis, highlighting recent advances, challenges, and future multimodal integration prospects.

Contribution

It provides a comprehensive overview of LLM applications in rare disease diagnosis, emphasizing recent research, challenges, and future multimodal data integration opportunities.

Findings

LLMs effectively extract relevant medical information.

LLMs enable intelligent patient interactions.

Experimentation with multiple LLMs shows diagnostic potential.

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.