ReviewiScience2026
Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.
Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is transforming pediatric healthcare, offering novel opportunities for early diagnosis, personalized treatment, and more efficient clinical workflows. However, its integration into children's health faces significant challenges due to the unique developmental, biological, and ethical considerations involved. This review explores how AI, leveraging large-scale real-world data such as electronic health records (EHRs), can augment pediatric clinical decision-making, risk stratification, communication, and workflow under human oversight. We examine its current applications and potential to improve pediatric care in areas including disease diagnosis, prediction, prevention, and personalized treatment. Additionally, we evaluate the role of AI in accelerating pediatric drug discovery and in supporting global health and epidemic management for children. Despite these promising advancements, significant barriers, such as data scarcity, ethical dilemmas, and the interpretability challenges posed by "black box" models, must be addressed to enable widespread adoption.
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.