ArticleGenome medicine2021
Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.
Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses that pooled it.
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Who cites it
88 citing papers in PubMed, 2 syntheses or guidelines pooled it, 141 citations in OpenAlex.
- Artificial Intelligence Applications in Medical Devices for Personalized Health Care Solutions: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review.Applied clinical informatics · 2025Pooled it
- Balancing automation and expertise: real-world evaluation of AI-assisted whole-exome sequencing in lupus-causing genes.Lupus science & medicine · 2026Article
- Rare variant analysis of whole genome sequenced juvenile idiopathic arthritis multiplex pedigrees identifies rare variants in NOD2 and ACVR1.G3 (Bethesda, Md.) · 2026Article
- Artificial intelligence in clinical genetics: Current practice and attitudes among the clinical genetics workforce.Genetics in medicine : official journal of the American College of Medical Genetics · 2026Article
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Rapid genomic sequencing in the NICU: Who to test and why.European journal of human genetics : EJHG · 2026Review
- Integration of Machine Learning-Based Pathogenicity Prediction and Phenotype Matching Improves Variant Prioritization in Rare Clinical Testing.Current issues in molecular biology · 2026Article
- Genetic contributions to mitochondrial dysfunction in amyotrophic lateral sclerosis etiology.HGG advances · 2026Article
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 2026Article
- Article
- Precision Grounding: augmenting large language models with evidence-based databases for trustworthy genetic variant summarization.International journal of medical informatics · 2026Article
- Review
- Artificial Intelligence in Inherited Epidermolysis Bullosa: Current Evidence, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- Automating genomic reanalysis: perspectives of people living with, or impacted by, a genetic, rare or undiagnosed condition.BMC medical ethics · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Artificial intelligence in genomic medicine: dispelling three myths.NPJ genomic medicine · 2026Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Nonlinear Bivariate Associations and Mononuclear Cell-Type-Specific Expression Level Differences in the STING Signalling Pathway.Journal of cellular and molecular medicine · 2026Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
28 authors at 9 institutions in 3 countries.
Funding
Abstract
backgroundClinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation.
methodsWe benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed.
resultsGEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases.
conclusionsGEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.
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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.