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

arXiv: https://arxiv.org/abs/2505.17065 · HTML: https://mlsanity.com/paper/2505.17065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 18 May 2025.

## Builds on (most cited first, 15 of 15)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [RareBench: Can LLMs Serve as Rare Diseases Specialists?](https://mlsanity.com/paper/2402.06341/) (2024)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms](https://mlsanity.com/paper/2108.01204/) (2021)
- [Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication](https://mlsanity.com/paper/2401.01620/) (2024)
- [Large Language Models Vote: Prompting for Rare Disease Identification](https://mlsanity.com/paper/2308.12890/) (2023)
- [Deep Learning for Genomics: A Concise Overview](https://mlsanity.com/paper/1802.00810/) (2018)
- [A Hybrid Framework with Large Language Models for Rare Disease Phenotyping](https://mlsanity.com/paper/2405.10440/) (2024)
- [Zebra-Llama: A Context-Aware Large Language Model for Democratizing Rare Disease Knowledge](https://mlsanity.com/paper/2411.02657/) (2024)
- [Understanding Sarcoidosis Using Large Language Models and Social Media Data](https://mlsanity.com/paper/2405.13005/) (2024)

## Abstract (arXiv)

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.