TL;DR
With continued progress in precision medicine and genomics, these AI genomic tools are likely to be integrated more into medical genetics practice, rather than remaining restricted to specialised or experimental settings.
A clinically oriented, pipeline-based synthesis of contemporary AI applications in genomic medicine, focusing on factors that determine model robustness and clinical utility, and common sources of failure in real-world genomic AI systems.
One of the main challenges in the field of rare diseases (RDs) remains the persistent lack of timely and accurate diagnoses. Currently, it is estimated that over half of patients remain undiagnosed. The recent development of high-throughput omics technologies, such as genomics, transcriptomics, epigenomics, proteomics, and metabolomics, is transforming the diagnosis and research of rare genetic diseases. These technologies allow for a deeper understanding of the underlying molecular mechanisms and greatly improve diagnostic accuracy. This review outlines a comprehensive clinical workflow that integrates deep phenotyping, genomic variant identification, and functional validation with multi-omics approaches to enhance diagnostic accuracy in RDs. We detail the key methodologies, bioinformatics tools, and developmental processes used in omics, focusing on their roles in identifying and prioritizing candidate variants, interpreting variants of uncertain significance, and generating clinically relevant information. Furthermore, we highlight the importance of both targeted and non-targeted functional validation strategies, which provide essential evidence of pathogenicity. The integration of multi-omics approaches will improve our understanding of RDs, enhance diagnostic accuracy in clinical settings, and lay the foundation for future therapeutic development within the framework of precision medicine.
This Review highlights AI and ML frameworks for integrating genomic, multi-omics, and EHR data, and discusses how these approaches are reshaping genomics research as well as clinical practice.
PURPOSE OF REVIEW Artificial intelligence (AI) is being rapidly but unevenly integrated into many aspects of medicine as well as society more broadly. This review focuses on recent developments in AI through the lens of how it is affecting the field of pediatric genomics. RECENT FINDINGS This article describes and explains general definitions of AI and its subtypes, including related to the latest AI applications. The article then outlines ways in which AI is being used and tested in multiple aspects of pediatric genomics, including: identifying individuals who may have genetic conditions and assisting in the diagnostic process; AI implementation in the laboratory-based genetic testing process; how recent AI developments may support patient management as well as discovery and research into novel ways to treat genetic conditions. Overall, AI is being applied to pediatric genomics in many different ways. SUMMARY The field of pediatrics will undergo dramatic change because of AI. This piece traces different ways in which AI may affect one particular field, pediatric genomics. Due to rapid advances, it is likely that future AI discoveries will lead to additional ways in which AI affects this field.
Prenatal genomic medicine now generates data at a scale that exceeds what unaided human interpretation can process within the time available to a continuing pregnancy. Cell-free DNA screening, chromosomal microarray, exome sequencing and, increasingly, genome sequencing produce millions of candidate observations per case, and computational methods described as artificial intelligence have been proposed at almost every step of the resulting pipeline. This critical narrative review evaluates the strength, consistency and clinical relevance of the evidence supporting those methods when they are applied specifically to the genome of a fetus in utero. Literature was identified through structured searching of biomedical and multidisciplinary scholarly sources from 1997 to 20 June 2026, prioritised by methodological quality and by direct relevance to prenatal application rather than by citation count. The evidence divides sharply. Analytical performance for tasks that are essentially signal-detection problems, including trisomy classification from low-coverage plasma sequencing, fetal fraction estimation and small-variant calling, is supported by internally consistent and technically credible work. Evidence for tasks that require inference about meaning, including missense and splice-effect prediction, phenotype-driven gene prioritisation and automated candidate diagnosis, is substantially weaker in the prenatal context, because the models were trained and calibrated on postnatal cohorts whose phenotypes were fully observable. Fetal phenotypes evolve during gestation, are captured through imaging rather than examination, and map imperfectly onto the ontologies that phenotype-driven algorithms consume. Several studies show that ontology-driven gene selection in fetuses misses a substantial minority of causative genes that curated prenatal panels retain. Almost no prospective study has tested whether an artificial intelligence component changes a prenatal decision, and reporting quality across the field falls short of contemporary prediction-model standards. Structural inequities in reference genomic resources compound these problems. Priorities include prenatally calibrated variant-effect evidence, gestational-age-aware phenotype representation, prospective evaluation with decision-relevant endpoints, and governance that treats uncertainty as a reportable output rather than a defect to be smoothed away.
aiDIVA is presented, an ensemble-AI combining statistical and machine learning models trained on genomic and phenotypic data to identify causal variants among tens of thousands per patient, and applies a random forest model to classify pathogenicity and generates evidence-based scores for dominant and recessive diseases.
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