MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1016/j.landig.2025.100953

Медицинская библиотека · The Lancet. Digital health · 2026

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

Chen Haichao, Zhao Zhengyun, Zhou Songchi, Hu Shikai, Wang Jinyuan, Jin Ye, Jin Xianghong, Tham Yih Chung, Wang Xiaofei, Ma Weizhi, Wu Honghan, Sheng Bin, Zhang Shuyang, Yu Sheng, Wong Tien Yin

СтатьяОбзорПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

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.

Для цитирования: Chen Haichao, Zhao Zhengyun, Zhou Songchi, Hu Shikai, Wang Jinyuan, Jin Ye, Jin Xianghong, Tham Yih Chung, Wang Xiaofei, Ma Weizhi, Wu Honghan, Sheng Bin, Zhang Shuyang, Yu Sheng, Wong Tien Yin. RareArena: a comprehensive benchmark dataset unveiling the potential of large language models in rare disease diagnosis.. The Lancet. Digital health. 2026. DOI: 10.1016/j.landig.2025.100953.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.