RareArena: a comprehensive benchmark dataset unveiling the potential of large language models in rare disease diagnosis

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Authors: Haichao Chen, Zhengyun Zhao, Songchi Zhou, Shikai Hu, Jinyuan Wang, Ye Jin, Xianghong Jin, Yih Chung Tham, Xiaofei Wang, Weizhi Ma, Jinge Wu, Bin Sheng
+3 more Shimin Zhang, Sheng Yu, Tien Yin Wong
Cited by: 3
References: 22

Abstract

Rare diseases pose a substantial clinical and public health burden, with timely and accurate diagnoses remaining a formidable challenge in many countries and settings. Large language models (LLMs) have the potential to enhance the screening and diagnosis of rare diseases. However, existing evaluations of such capacities of LLMs are insufficient due to the low sample sizes, poor coverage of different rare diseases, and weak real-world clinical relevance. To address these issues, we developed an automated data collection pipeline, RareArena, to curate a comprehensive rare disease dataset from PubMed Central case reports. Two tasks were considered: rare disease screening (RDS), which involved rare disease recognition before confirmatory diagnostic tests; and rare disease confirmation (RDC), which required diagnosis with complete information. The constructed RDS task comprised 49 760 cases of 4597 rare diseases, whereas the RDC task comprised 22 901 cases of 3522 rare diseases. Human evaluations by two physicians across three dimensions-leakage, fidelity, and complexity-validated the high quality of our dataset. We systematically benchmarked ten state-of-the-art LLMs of varying sizes (GPT-4o, Llama3·1-70B, Llama3·1-8B, Qwen2·5-72B, Qwen2·5-7B, Gemma2-9B, Phi3-7B, Llama3-70B-UltraMedical, Llama3-8B-UltraMedical, and MedLlama-7B) and found that GPT-4o achieved the best performance in both the tasks (RDS: 33·1% top-1 recall, 56·9% top-5 recall; RDC: 64·2% top-1 recall, 85·9% top-5 recall). Additionally, GPT-4o performed better on genetically inherited diseases than on non-genetically inherited diseases in both the tasks, with its highest performance observed in systemic or rheumatologic diseases (RDS: 48·5% top-1 recall, 76·5% top-5 recall; RDC: 76·0% top-1 recall, 93·5% top-5 recall). To our knowledge, our dataset represents the largest rare disease diagnostic benchmark to date, featuring broad and deep disease coverage and reflecting clinically relevant scenarios. RareArena highlights the potential of LLMs in rare disease screening and diagnosis, providing a foundation for improved care for individuals with rare diseases around the world.

Fields of Study & Subjects

Genomics and Rare Diseases Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare

Publication details

Journal / source
The Lancet Digital Health
Publisher
Elsevier BV
Volume
8
Issue
2
Pages
100953–100953
Publication date
1 February 2026
Language
EN