Evidence map›Paper›PMID 41168479›Full record

ReviewNPJ digital medicine2025

Toward governance of artificial intelligence in pediatric healthcare.

Felix Richter, Emma Holmes, Florian Richter, Katherine Guttmann, Son Q Duong, Sandeep Gangadharan, Eric E Schadt, Hojjat Salmasian, Bruce D Gelb, Benjamin S Glicksberg

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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  8. Article
  9. Article
  10. Article
  11. Preventing Medication Errors in Paediatrics: A Narrative Review.Clinical medicine insights. Pediatrics · 2026
    Review
  12. Article
  13. Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Felix Richter *The Center for AI in Children's Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Emma Holmes *The Center for AI in Children's Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Florian RichterArtemisAI Labs, San Diego, CA, USA.
Katherine GuttmannThe Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Son Q DuongThe Center for AI in Children's Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Sandeep GangadharanThe Center for AI in Children's Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Eric E SchadtThe Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Hojjat SalmasianChildren's Hospital of Philadelphia, Philadelphia, PA, USA.
Bruce D GelbThe Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Benjamin S GlicksbergThe Center for AI in Children's Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. benjamin.glicksberg@mssm.edu.

Funding

NIH HHS 5KL2TR004421-03
6 · The paper itself

Abstract

AI is transforming healthcare, yet pediatric adoption remains limited and governance is underdeveloped. We review existing frameworks and identify pediatric-specific gaps: insufficient stakeholder engagement, developmentally appropriate consent/assent, limited bias mitigation, and unclear accountability. An analysis of FDA-cleared pediatric SaMDs shows radiology dominance while other specialties lag. We call for a pediatric-centric governance approach emphasizing transparency, inclusive participation, equitable data practices, and rigorous post-deployment monitoring to ensure safe, responsible integration.

Identifiers

PMID41168479
PMCPMC12575634

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.