ArticlePublic health challenges2025
Can AI Bridge or Widen Maternal Health Inequities?
Article in Public health challenges, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.The pharmacogenomics journal · 2026Review
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Artificial intelligence and the future of maternal and newborn health in low-income countries: advancing equity, early detection, and health system resilience.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is rapidly transforming maternal healthcare through tools like risk prediction algorithms, telemedicine platforms, and postpartum support chatbots. Although these innovations offer promise, particularly in low- and middle-income countries (LMICs), their impact on health equity remains contested. This commentary explores how AI can either bridge or widen maternal health inequities, depending on how it is designed, governed, and implemented. We introduce a conceptual framework comprising four interdependent domains that shape equity outcomes in maternal health: inclusive data practices, equitable governance, participatory design, and local capacity-building. Drawing from interdisciplinary literature, we situate AI within broader health and social systems and argue for equity-oriented approaches that foreground representation, accountability, and community engagement. By examining both opportunities and risks, this commentary offers practical, context-sensitive recommendations for LMICs to ensure AI serves as a tool for justice in maternal healthcare.
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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.