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

Abstract Large language models (LLMs) have shown promising capabilities in transforming rare disease research. This survey paper explores the integra- tion of LLMs in analyzing 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 is a promising frontier. We review foundational papers demonstrating the application of LLMs in identifying and extracting relevant medical infor- mation, simulating intelligent conversational agents for patient interac- tion, and formulating accurate and timely diagnoses. Furthermore, this paper discusses the challenges and ethical considerations inherent in de- ploying 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 struc- tured 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 inte- grate diverse data types to provide a more comprehensive understanding of rare diseases, ultimately fostering better outcomes in clinical settings.