ReviewThe pharmacogenomics journal2026
Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.
Review in The pharmacogenomics journal, 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
1 author.
Funding
Abstract
Maternal and neonatal health (MNH) urgently requires precision medicine interventions, as morbidity, mortality, and health disparities hinder the achievement of Sustainable Development Goal 3. Clinical implementation of artificial intelligence (AI)-powered Pharmacogenomics (PGx) requires validated, transparent algorithms and frameworks. The "pregnancy black box"-which refers to a data void due to historical exclusion of pregnant and postpartum women from clinical trials-continues to create bias in AI models. The review establishes a path for upcoming research, including methods to reduce algorithmic bias via AI-driven data augmentation, resolution of ethical challenges, and creation of international registries. Ultimately, leveraging AI for remote monitoring is crucial for enhancing equitable access in lower-resource environments. The proposed roadmap provides organizations with a robust framework to develop AI-driven PGx systems, which will enable safer and more tailored pharmacotherapy for mothers and their newborns.
Indexed as
Identifiers
What Socratic 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 Socratic graph.