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References from Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review. Local targets link to admitted publications; unresolved targets remain external evidence.
Time to diagnosis and determinants of diagnostic delays of people living with a rare disease: results of a rare barometer retrospective patient survey
10.1038/s41431-024-01604-z · External reference
Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database
10.1038/s41431-019-0508-0 · External reference
Identifying facial phenotypes of genetic disorders using deep learning
10.1038/s41591-018-0279-0 · External reference
Large language models for disease diagnosis: a scoping review
10.1038/s44387-025-00011-z · External reference
Deep learning for rare disease: a scoping review
10.1016/j.jbi.2022.104227 · External reference
The use of machine learning in rare diseases: a scoping review
10.1186/s13023-020-01424-6 · External reference
Artificial intelligence in the diagnosis of pediatric rare diseases: from real-world data toward a personalized medicine approach
10.3390/jpm15090407 · External reference
Updated methodological guidance for the conduct of scoping reviews
10.11124/jbies-20-00167 · External reference
PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation
10.7326/m18-0850 · External reference
Scoping studies: towards a methodological framework
10.1080/1364557032000119616 · External reference
China has officially released its first national list of rare diseases
10.5582/irdr.2018.01056 · External reference
Machine learning-based prediction of inborn errors of immunity in patients with low IgE levels
10.1111/pai.70283 · External reference
Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
10.1038/s41746-025-01749-1 · External reference
Functional data strengthen clinical validation of PhenoScore phenotype-guided AI for ANKRD11 missense variants
10.1111/cge.70159 · External reference
Genomic DNA methylation signatures enable concurrent diagnosis and clinical genetic variant classification in neurodevelopmental syndromes
10.1016/j.ajhg.2017.12.008 · External reference
Evaluation of a new inclusive next-generation synthetic face tool for dysmorphology
10.1002/ajmg.a.64298 · External reference
10.64898/2026.03.25.26349289
10.64898/2026.03.25.26349289 · External reference
Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia
10.1186/s13023-025-03966-z · External reference
Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database
10.1002/pbc.30260 · External reference
Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders
10.1038/s41746-024-01331-1 · External reference
RareArena: a comprehensive benchmark dataset unveiling the potential of large language models in rare disease diagnosis
10.1016/j.landig.2025.100953 · External reference
Patient-patient similarity-based screening of a clinical data warehouse to support ciliopathy diagnosis
10.3389/fphar.2022.786710 · External reference
SABPI-Net: a structure-aware bidirectional proxy interaction network for infantile retinal disease diagnosis
10.1109/tmi.2026.3650968 · External reference
Identification of atypical scoliosis patterns using X-ray images based on fine-grained techniques in deep learning
10.1177/21925682251349999 · External reference
Genomic answers for children: dynamic analyses of >1000 pediatric rare disease genomes
10.1016/j.gim.2022.02.007 · External reference
Epidemiological analysis to identify predictors of X-linked hypophosphatemia (XLH) diagnosis in an Italian pediatric population: the EPIX project
10.1007/s12020-024-03793-5 · External reference
Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study
10.1007/s00247-026-06638-7 · External reference
10.64898/2026.03.03.26346563
10.64898/2026.03.03.26346563 · External reference
Enhancing TSH-based congenital hypothyroidism screening using machine learning and resampling algorithms
10.1186/s12911-025-03312-0 · External reference
Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases
10.1186/s13073-021-00965-0 · External reference
AI-Based facial phenotyping supports a shared molecular axis in PACS1-, PACS2-, and WDR37-related syndromes
10.3390/ijms26167964 · External reference
PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework
10.1038/s41588-023-01469-w · External reference
Population-specific facial traits and diagnosis accuracy of genetic and rare diseases in an admixed Colombian population
10.1038/s41598-023-33374-x · External reference
Success of face analysis technology in rare genetic diseases diagnosed by whole-exome sequencing: a single-center experience
10.1159/000505800 · External reference
Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis
10.1186/s13040-026-00523-7 · External reference
IESS-FusionNet: physiologically inspired EEG-EMG fusion with linear recurrent attention for infantile epileptic spasms syndrome detection
10.3390/bioengineering13010057 · External reference
Next generation phenotyping using narrative reports in a rare disease clinical data warehouse
10.1186/s13023-018-0830-6 · External reference
A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies
10.1038/s41746-026-02406-x · External reference
A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles
10.1186/s13023-025-03537-2 · External reference
Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants
10.1242/bio.060286 · External reference
An automatic facial landmarking for children with rare diseases
10.1002/ajmg.a.63126 · External reference
Estimating prevalence of rare genetic disease diagnoses using electronic health records in a children’s hospital
10.1016/j.xhgg.2024.100341 · External reference
From self-supervised learning to transfer learning with musculoskeletal radiographs
10.1515/cdbme-2022-1003 · External reference
Genetic syndromes screening by facial recognition technology: VGG-16 screening model construction and evaluation
10.1186/s13023-021-01979-y · External reference
GestaltMatcher facilitates rare disease matching using facial phenotype descriptors
10.1038/s41588-021-01010-x · External reference
Optimization and evaluation of facial recognition models for Williams-Beuren syndrome
10.1007/s00431-024-05646-9 · External reference
Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network
10.1002/ajmg.a.63495 · External reference
Automatic detection of genetic diseases in pediatric age using pupillometry
10.1109/access.2020.2973747 · External reference
The artificial intelligence-assisted diagnosis of skeletal dysplasias in pediatric patients: a comparative benchmark study of large language models and a clinical expert group
10.3390/genes16070762 · External reference
A recall-optimised machine learning framework for small data improves risk stratification for Hirschsprung’s disease
10.1016/j.imu.2024.101530 · External reference
Graph retrieval augmented large language models for facial phenotype associated rare genetic disease
10.1038/s41746-025-01955-x · External reference
Development and validation of a minimally invasive diagnostic model for biliary atresia using artificial intelligence
10.1007/s12519-025-00988-2 · External reference
i-Dent: a virtual assistant to diagnose rare genetic dental diseases
10.1016/j.compbiomed.2024.108927 · External reference
InfEHR: clinical phenotype resolution through deep geometric learning on electronic health records
10.1038/s41467-025-63366-6 · External reference
Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers
10.1186/s11689-022-09434-0 · External reference
A minimalist and robust diagnostic model for neonatal biliary atresia: harnessing MMP-7 and machine learning in a time-critical setting
10.1016/j.jpedsurg.2026.163090 · External reference
Machine learning algorithms integrate single-cell and bulk RNA data to reveal the immuno-molecular characteristics of mast cells in eosinophilic esophagitis [Article in Chinese]
10.20047/j.issn1673-7210.25091014 · External reference
Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC–MS urinary metabolomics for diseases screening
10.1016/j.ijmedinf.2024.105765 · External reference
Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia
10.1186/s12916-025-03962-x · External reference
A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs
10.1038/s41746-025-01452-1 · External reference
Who’s your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry
10.1016/j.clim.2023.109759 · External reference
SSL-OHE: a self-supervised ensemble approach for early diagnosis of biliary atresia from sonographic images
10.1016/j.bspc.2025.108539 · External reference
Proteome-wide model for human disease genetics
10.1038/s41588-025-02400-1 · External reference
Sequence-structure based prediction of pathogenicity for amino acid substitutions in proteins associated with primary immunodeficiencies
10.3389/fimmu.2025.1492751 · External reference
Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study
10.1016/s2589-7500(21)00137-0 · External reference
Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias
10.1007/s00247-023-05789-1 · External reference
Open-source artificial intelligence system supports diagnosis of Mendelian diseases in acutely ill infants
10.3390/children10060991 · External reference
PI Prob: a risk prediction and clinical guidance system for evaluating patients with recurrent infections
10.1371/journal.pone.0237285 · External reference
Disease-associated and patient-specific immune cell signatures in juvenile-onset systemic lupus erythematosus: patient stratification using a machine-learning approach
10.1016/s2665-9913(20)30168-5 · External reference
Artificial intelligence software changes rare disease testing strategy in real time: an international case series using Face2Gene
10.1002/ajmg.a.64171 · External reference
Deep learning-based early detection of rare diseases using electronic health records
10.63682/jns.v14i14s.3653 · External reference
Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings
10.1038/s41588-024-01836-1 · External reference
Machine learning-assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profiling
10.1016/j.jaci.2025.10.022 · External reference
An explainable dataset linking facial phenotypes and genes to rare genetic diseases
10.1038/s41597-025-04922-z · External reference
Multiscore, a gene ranker powered by artificial intelligence and real-world clinical data, shows high sensitivity for the molecular diagnosis of Mendelian disorders in nearly 10,000 exomes and genomes
10.1007/s00439-025-02797-z · External reference
Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders
10.1038/s41436-018-0404-y · External reference
Artificial intelligence enhances diagnostic accuracy of contrast enemas in Hirschsprung disease compared to clinical experts
10.1055/a-2646-2052 · External reference
Research and implementation of an intelligent rare disease discovery system driven by large language models [Article in Chinese]
10.3969/j.issn.1673-7571.2026.03.005 · External reference
A semantic similarity algorithm using opposite phenotypes for the auxiliary diagnosis of rare diseases [Article in Chinese]
10.13417/j.gab.039.005868 · External reference
Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning
10.1186/s12920-025-02261-x · External reference
The application of the facial analysis program Face2Gene in a single genetic counseling center: a retrospective study
10.1186/s43054-025-00344-z · External reference
Establishment of an auxiliary diagnosis system of newborn screening for inherited metabolic diseases based on artificial intelligence technology and a clinical trial
10.3760/cma.j.cn112140-20201209-01089 · External reference
The significance of machine learning in neonatal screening for inherited metabolic diseases
10.3389/fped.2024.1366891 · External reference
Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients
10.1016/j.gim.2022.08.002 · External reference
Finding commonalities in rare diseases through the undiagnosed diseases network
10.1093/jamia/ocab050 · External reference
Diagnostic accuracy of a custom large language model on rare pediatric disease case reports
10.1002/ajmg.a.63878 · External reference
Study on the method of using facial features to improve the accuracy of rare disease prediction in small sample scenarios [Article in Chinese]
10.1016/j.heliyon.2024.e39479 · External reference
Digital-tier strategy improves newborn screening for glutaric aciduria type 1
10.3390/ijns10040083 · External reference
Unresolved reference
External reference
An agentic system for rare disease diagnosis with traceable reasoning
10.1038/s41586-025-10097-9 · External reference
ADHTransNet-based radiomics on multimodal pituitary MRI for non-invasive hormone prediction in children
10.1016/j.cmpb.2026.109235 · External reference
An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases
10.1007/s00439-023-02638-x · External reference
