ReviewOrphanet journal of rare diseases2025
Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.
Review in Orphanet journal of rare diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Review
- Artificial Intelligence in Inherited Epidermolysis Bullosa: Current Evidence, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Reimagining care of people living with rare diseases with artificial intelligence.PLoS medicine · 2026Article
- Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis.BioData mining · 2026Article
- The pediatric AI readiness framework: bridging evidence to practice in pediatric artificial intelligence.Frontiers in artificial intelligence · 2026Article
- From Variant Interpretation to Biomarker Translation: Multi-omics Integration in Inherited Neuromuscular Diseases.Human mutation · 2026Review
- Integrating genomic insights and artificial intelligence to elucidate lipoprotein(a)-mediated risk in cardio-renal syndrome.Annals of medicine and surgery (2012) · 2026Article
- Expert opinion on facilitating intrafamily communication in rare diseases-Lessons from Fabry disease.Genetics in medicine open · 2026Article
- An in silico protocol for predicting genetic biomarkers in rare diseases: a case study in sporadic amyotrophic lateral sclerosis.Frontiers in genetics · 2026Article
- ZebraMap: A Multimodal Rare Disease Knowledge Map with Automated Data Aggregation & LLM-Enriched Information Extraction Pipeline.Diagnostics (Basel, Switzerland) · 2025Article
- Leveraging AI to Enhance Electronic Health Records.EJIFCC · 2025Review
- Epigenetic Mechanisms in Fabry Disease: A Thematic Analysis Linking Differential Methylation Profiles and Genetic Modifiers to Disease Phenotype.Current issues in molecular biology · 2025Review
- Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach.Journal of personalized medicine · 2025Review
- A Multi-Stage Framework for Kawasaki Disease Prediction Using Clustering-Based Undersampling and Synthetic Data Augmentation: Cross-Institutional Validation with Dual-Center Clinical Data in Taiwan.Bioengineering (Basel, Switzerland) · 2025Article
- Bioinformatics-Driven Multi-Factorial Insight into α-Galactosidase Mutations.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUse 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.
methodsWe 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.
resultsIn 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.
conclusionAI-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.