Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Artificial intelligence; Deep learning; Fabry disease; Machine learning; Rare diseases.

Fig. 1

Scheme of the machine learning common process combining human input and computer activities. The algorithm is first trained with a set of raw data that are pre-processed automatically and presented. Generated pre-processed data are validated and corrected manually by an AI expert. These annotated data are distributed for use as the training dataset (65%), for validation (15%) and to test the machine learning process (20%) that trains the model, validates the hyperparameter settings, and evaluates and applies the model in production This process generates processed data such as classified images, extracted features, recognized name entities, and segmented images. The model is then applied in production to other sets of automatically pre-processed data