Germain Dominique P, Gruson David, Malcles Marie, Garcelon Nicolas
Division of Medical Genetics, University of Versailles-St Quentin en Yvelines (UVSQ), Paris-Saclay University, 2 avenue de la Source de la Bièvre, 78180, Montigny, France.
First Faculty of Medicine, Charles University, Prague, Czech Republic.
Orphanet J Rare Dis. 2025 Apr 17;20(1):186. doi: 10.1186/s13023-025-03655-x.
Use of artificial intelligence (AI) in rare diseases has grown rapidly in recent years. In this review we have outlined the most common machine-learning and deep-learning methods currently being used to classify and analyse large amounts of data, such as standardized images or specific text in electronic health records. To illustrate how these methods have been adapted or developed for use with rare diseases, we have focused on Fabry disease, an X-linked genetic disorder caused by lysosomal α-galactosidase. A deficiency that can result in multiple organ damage.
We searched PubMed for articles focusing on AI, rare diseases, and Fabry disease published anytime up to 08 January 2025. Further searches, limited to articles published between 01 January 2021 and 31 December 2023, were also performed using double combinations of keywords related to AI and each organ affected in Fabry disease, and AI and rare diseases.
In total, 20 articles on AI and Fabry disease were included. In the rare disease field, AI methods may be applied prospectively to large populations to identify specific patients, or retrospectively to large data sets to diagnose a previously overlooked rare disease. Different AI methods may facilitate Fabry disease diagnosis, help monitor progression in affected organs, and potentially contribute to personalized therapy development. The implementation of AI methods in general healthcare and medical imaging centres may help raise awareness of rare diseases and prompt general practitioners to consider these conditions earlier in the diagnostic pathway, while chatbots and telemedicine may accelerate patient referral to rare disease experts. The use of AI technologies in healthcare may generate specific ethical risks, prompting new AI regulatory frameworks aimed at addressing these issues to be established in Europe and the United States.
AI-based methods will lead to substantial improvements in the diagnosis and management of rare diseases. The need for a human guarantee of AI is a key issue in pursuing innovation while ensuring that human involvement remains at the centre of patient care during this technological revolution.
近年来，人工智能（AI）在罕见病领域的应用迅速增长。在本综述中，我们概述了目前用于分类和分析大量数据（如标准化图像或电子健康记录中的特定文本）的最常见机器学习和深度学习方法。为了说明这些方法如何被改编或开发用于罕见病，我们重点关注了法布里病，这是一种由溶酶体α-半乳糖苷酶缺乏引起的X连锁遗传病。这种缺乏会导致多器官损伤。
我们在PubMed上搜索截至2025年1月8日发表的关注人工智能、罕见病和法布里病的文章。还使用与人工智能和法布里病中受影响的每个器官相关的关键词双组合，以及人工智能和罕见病，进行了进一步搜索，限于2021年1月1日至2023年12月31日发表的文章。
总共纳入了20篇关于人工智能和法布里病的文章。在罕见病领域，人工智能方法可前瞻性地应用于大量人群以识别特定患者，或回顾性地应用于大型数据集以诊断先前被忽视的罕见病。不同的人工智能方法可能有助于法布里病的诊断，帮助监测受影响器官的进展，并可能有助于个性化治疗的开发。在一般医疗保健和医学影像中心实施人工智能方法可能有助于提高对罕见病的认识，并促使全科医生在诊断过程中更早地考虑这些疾病，而聊天机器人和远程医疗可能会加速患者转诊至罕见病专家。在医疗保健中使用人工智能技术可能会产生特定的伦理风险，促使欧洲和美国建立旨在解决这些问题的新人工智能监管框架。
基于人工智能的方法将在罕见病的诊断和管理方面带来实质性改善。在追求创新的同时确保人工智能的人为保障是一个关键问题，在此技术革命期间确保人类参与仍处于患者护理的核心地位。
