Related Experiment Video
Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
RareArena: a comprehensive benchmark dataset unveiling the potential of large language models in rare disease
Haichao Chen1, Zhengyun Zhao2, Songchi Zhou2
1Beijing Visual Science and Translational Eye Research Institute (BERI), Beijing Tsinghua Changgung Hospital Eye Center, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China; Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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.
More Related Videos
05:47Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
07:35A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023