Parenting Journal · Volume 2 · Issue 5 · Licensed source edition
Applied artificial intelligence for global child health: Addressing biases and barriers
Originally published 2024-08-22 · PLOS Digital Health
PLOS · DOI 10.1371/journal.pdig.0000583 ↗
AI / system type
Mixed narrow healthcare AI/ML; narrative equity analysis
Family implicationsDesign, ethics, stakeholder or technical evidence with child/family implications; household benefits are not assumed.
The AI connection
The review explicitly evaluates AI/ML applications and barriers in global pediatric research and care.
Why this reading matters
Broadens respect from individual consent to inclusion in the institutions and data shaping pediatric AI.
Editorial reading guide · Respect
Who gains access to the benefits of child-health AI, and who helps design it?
The authors argue that pediatric needs and local contexts remain underrepresented in AI development concentrated in wealthy countries. They connect equitable deployment to caregiver and youth participation, local capacity, and existing health-system needs.
Study context
This review synthesizes technical, institutional, and governance barriers; it is not a new child or caregiver cohort.
Age / stage: Children and young people in high- and lower-resource health systems
Read with three questions
- How do pediatric data needs differ from adult model development?
- Why might a technically promising tool fail in a lower-resource care system?
- What would meaningful participation by local families change?
Keep the limits in view
This is a perspective-based narrative review without systematic evidence grading or formal consensus. Proposed governance and collaboration strategies are reasoned recommendations, not demonstrated improvements in access or child outcomes.
The text below is the original article’s licensed repository edition. Its wording is not rewritten. Use the Paper Agent beside the text to locate evidence; open the source for figures, tables and complete context.
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Original authorship, publication date, journal and DOI are preserved. This issue’s TRICK placement and reading questions are editorial additions, not claims made by the original authors.
Vijaytha Muralidharan; Joel Schamroth; Alaa Youssef; Leo A Celi; Roxana Daneshjou; Walter Karlen (2024). Applied artificial intelligence for global child health: Addressing biases and barriers PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0000583
Permissions checked against the saved article notice; repository publication notices screened 2026-11-04. This is not a new peer-review decision. The source authors and publishers are not represented as endorsing this issue.