Artificial Intelligence in Perinatal Medicine: A Systematic Review of Current Applications, Limitations, and a Translational Roadmap for the Foundation-Model Era
Bibliographic record
Abstract
Abstract Artificial intelligence (AI) is increasingly applied across perinatal care, yet the maturity of the evidence base and its readiness for routine practice remain uncertain. We conducted a preferred reporting items for systematic reviews and meta-analyses (PRISMA)-2020 systematic review to map applications, appraise quality, and outline translational requirements. We searched PubMed/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, Cochrane Library, ClinicalTrials.gov/ICTRP, and medRxiv/bioRxiv from 2000 to 2 September 2025. Two reviewers independently screened records and extracted data, with disagreements resolved by a third reviewer. Eligibility criteria included human perinatal studies reporting AI model development or validation, prospective cohorts or trials, detailed protocols with explicit AI methods, and systematic or scoping reviews on applications, ethics, or equity. Studies that were nonAI, nonperinatal, abstract-only, or nonEnglish without translation were excluded. Risk of bias was assessed using the Newcastle–Ottawa Scale (observational), A Measurement Tool to assess systematic reviews, version 2 (AMSTAR-2) (systematic reviews), and risk of bias in systematic reviews (ROBIS) (reviews/scoping reviews). Heterogeneity precluded meta-analysis; synthesis followed synthesis without meta-analysis (SWiM) principles. Thirty-six studies met inclusion criteria, with twenty designated as a pre-specified “core” set based on decision relevance and quality. Applications spanned preconception (fertility, maternal risk), antenatal (FGR, preeclampsia, preterm birth, anomalies), intrapartum (delivery mode/timing, fetal monitoring), and neonatal outcomes (pulmonary hemorrhage, composite morbidity). Across imaging-plus-clinical and EHR-based models, discrimination often exceeded baseline tools, while calibration, external or temporal validation, subgroup performance, code/data availability, and impact evaluation were inconsistently reported. Limitations include retrospective designs, single-site datasets, outcome heterogeneity, English-language restriction, and publication bias. AI in perinatal medicine shows technical promise but uneven clinical readiness. We propose a staged roadmap emphasizing standardized data and reporting, multi-site and temporal validation with recalibration, interoperable workflow delivery, privacy-preserving and fair learning, and continuous calibration, uncertainty, and drift monitoring. Registration: none; funding: none.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.116 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".