Mao Xiaohao, Huang Yu, Jin Ye, Wang Lun, Chen Xuanzhong, Liu Honghong, Yang Xinglin, Xu Haopeng, Luan Xiaodong, Xiao Ying, Feng Siqin, Zhu Jiahao, Zhang Xuegong, Jiang Rui, Zhang Shuyang, Chen Ting
Department of Computer Science and Technology & Institute for Artificial Intelligence & BNRist, Tsinghua University, Beijing, China.
Tencent Jarvis Lab, Shenzhen, China.
NPJ Digit Med. 2025 Jan 28;8(1):68. doi: 10.1038/s41746-025-01452-1.
Rare diseases, affecting ~350 million people worldwide, pose significant challenges in clinical diagnosis due to the lack of experienced physicians and the complexity of differentiating between numerous rare diseases. To address these challenges, we introduce PhenoBrain, a fully automated artificial intelligence pipeline. PhenoBrain utilizes a BERT-based natural language processing model to extract phenotypes from clinical texts in EHRs and employs five new diagnostic models for differential diagnoses of rare diseases. The AI system was developed and evaluated on diverse, multi-country rare disease datasets, comprising 2271 cases with 431 rare diseases. In 1936 test cases, PhenoBrain achieved an average predicted top-3 recall of 0.513 and a top-10 recall of 0.654, surpassing 13 leading prediction methods. In a human-computer study with 75 cases, PhenoBrain exhibited exceptional performance with a top-3 recall of 0.613 and a top-10 recall of 0.813, surpassing the performance of 50 specialist physicians and large language models like ChatGPT and GPT-4. Combining PhenoBrain's predictions with specialists increased the top-3 recall to 0.768, demonstrating its potential to enhance diagnostic accuracy in clinical workflows.
罕见病影响着全球约3.5亿人，由于缺乏经验丰富的医生以及区分众多罕见病的复杂性，在临床诊断中面临重大挑战。为应对这些挑战，我们引入了PhenoBrain，这是一个全自动的人工智能流程。PhenoBrain利用基于BERT的自然语言处理模型从电子健康记录（EHR）中的临床文本中提取表型，并采用五种新的诊断模型对罕见病进行鉴别诊断。该人工智能系统是在包含2271例431种罕见病的多国家、多样的罕见病数据集上开发和评估的。在1936个测试病例中，PhenoBrain的平均预测前3召回率为0.513，前10召回率为0.654，超过了13种领先的预测方法。在一项针对75个病例的人机研究中，PhenoBrain表现出色，前3召回率为0.613，前10召回率为0.813，超过了50名专科医生以及ChatGPT和GPT - 4等大型语言模型的表现。将PhenoBrain的预测结果与专家的判断相结合，可将前3召回率提高到0.768，证明了其在临床工作流程中提高诊断准确性的潜力。
