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

• URL: http://arxiv.org/abs/2505.17065v1
• Date: Sun, 18 May 2025 15:42:15 GMT
• Title: Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases
• Authors: Valentina Carbonari, Pierangelo Veltri, Pietro Hiram Guzzi,
• Abstract summary: Large language models (LLMs) have shown promising capabilities in transforming rare disease research.<n>This paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies.
• Score: 1.9662978733004604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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