ReviewGenes2019
Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?
Review in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review: Drug Repositioning for Congenital Disorders of Glycosylation (CDG).International journal of molecular sciences · 2022Pooled it
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- Challenges in the Classification of Cardiac Arrhythmias and Ischemia Using End-to-End Deep Learning and the Electrocardiogram: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- From Variant Interpretation to Biomarker Translation: Multi-omics Integration in Inherited Neuromuscular Diseases.Human mutation · 2026Review
- Self-adaptive forward-forward network for anomaly detection and medical image analysis.Frontiers in radiology · 2026Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- ChatGPT in Oral Pathology: Bright Promise or Diagnostic Mirage.Medicina (Kaunas, Lithuania) · 2025Article
- Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach.Journal of personalized medicine · 2025Review
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- Article
- AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction.Journal of imaging · 2025Review
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
- Ethical and Practical Considerations of Artificial Intelligence in Pediatric Medicine: A Systematic Review.Cureus · 2025Review
- Review
- An optimized deep learning model based on transperineal ultrasound images for precision diagnosis of female stress urinary incontinence.Frontiers in medicine · 2025Article
- Article
- Editorial: Exploring the future of neurology: how AI is revolutionizing diagnoses, treatments, and beyond.Frontiers in neurology · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.
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.