ReviewBiomedicines2023
The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.
Review in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 96 citations in OpenAlex.
- Quantifying the Functional Gap in Alkaptonuria Through Machine Learning and Clinical Data Integration.Bioengineering (Basel, Switzerland) · 2026Article
- Toward Early Diagnosis and Therapeutic Discovery in CLN3 Disease: A Computational Biomarker Discovery Framework.medRxiv : the preprint server for health sciences · 2026Article
- 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
- Decoding rare inherited metabolic disorders: advancing precision in screening and diagnosis.Orphanet journal of rare diseases · 2026Review
- External validation of an artificial intelligence tool for fracture detection in children with osteogenesis imperfecta: a multireader study.European radiology · 2026Article
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- From Variant Interpretation to Biomarker Translation: Multi-omics Integration in Inherited Neuromuscular Diseases.Human mutation · 2026Review
- ZebraMap: A Multimodal Rare Disease Knowledge Map with Automated Data Aggregation & LLM-Enriched Information Extraction Pipeline.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Argo Delphi consensus statement on red flags and clinical gateways towards rare disease diagnosis.Scientific reports · 2025Article
- Data Augmentation and Synthetic Data Generation in Rare Disease Research: A Scoping Review.Medical sciences (Basel, Switzerland) · 2025Article
- An integrated approach for rare disease detection and classification in Spanish pediatric medical reports.Scientific reports · 2025Article
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- Profiling of Protein-Coding Missense Mutations in Mendelian Rare Diseases: Clues from Structural Bioinformatics.International journal of molecular sciences · 2025Article
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
- Rare disease publishing trends worldwide and in China: A CiteSpace-based bibliometric study.Intractable & rare diseases research · 2025Article
- The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.Respiratory research · 2025Article
- Review
- Integrated Clinomics and Molecular Dynamics Simulation Approaches Reveal the SAA1.1 Allele as a Biomarker in Alkaptonuria Disease Severity.Biomolecules · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.
Indexed as
Identifiers
What Socratic holds
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.