ReviewNPJ digital medicine2019
Artificial intelligence for precision medicine in neurodevelopmental disorders.
Review in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 2 of them syntheses 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
84 citing papers in PubMed, 2 syntheses or guidelines pooled it, 235 citations in OpenAlex.
- Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.Frontiers in psychiatry · 2026Pooled it
- Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review.Frontiers in psychiatry · 2025Pooled it
- Precision medicine in low-income settings and small island developing states.Nature reviews. Endocrinology · 2026Review
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- A contrastive reconstruction vision transformer model for intelligent diagnosis of autism spectrum disorder.NPJ digital medicine · 2026Article
- Review
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Artificial intelligence in diagnosis of pediatric neurodevelopmental disorders: a scoping review.World journal of pediatrics : WJP · 2026Article
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.ACS omega · 2026Review
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- Health literacy and attitudes toward clinical AI: a cross-sectional study in Istanbul.Frontiers in digital health · 2026Article
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Review
- Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.Emerging topics in life sciences · 2025Article
- Precision therapies for genetic epilepsies in 2025: Promises and pitfalls.Epilepsia open · 2025Article
- An analytic research and review of the literature on practice of artificial intelligence in healthcare.European journal of medical research · 2025Review
- Big data for neuroscience in the context of predictive, preventive, and personalized medicine.The EPMA journal · 2025Review
- Genomic medicine and personalized treatment: a narrative review.Annals of medicine and surgery (2012) · 2025Review
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.