ReviewFrontiers in public health2026
How AI can be used to promote public and population health.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.The pharmacogenomics journal · 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
Here, we summarize the work that Microsoft's philanthropic Artificial Intelligence (AI) for Good Lab has completed in the realm of promoting public and population health. In particular, after providing examples of how the AI for Good Lab has articulated the value of using AI to improve public and population health, we provide examples and references of the work demonstrating how the Lab has: applied Artificial Intelligence (AI) to improve maternal, fetal, and infant health; leveraged large language models to improve population health; and applied AI to improve rural health and healthcare. We also summarize what we have learned through our work, finding that: getting the question right and ensuring the limitations of any analysis are understood is important; collaboration across public, private, and educational institutions with subject matter experts will be the most effective and efficient way to harness this new technology; and that focusing on metrics that reflect health, and not just the accuracy of the model, is the most impactful way to improve the health of populations, worldwide.
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.