SynthesisFrontiers in psychiatry2025
Artificial intelligence in the prevention and early detection of postpartum depression: a systematic review and meta-analysis.
Synthesis in Frontiers in psychiatry, 2025. 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.
- Conversational AI for perinatal mental health: promise, limits, and a human-AI stepped-care framework.Frontiers in psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Postpartum depression is a frequent complication after childbirth, affecting maternal health, infant development, and family well-being. This study evaluated the role of artificial intelligence (AI) in preventing and detecting postpartum depression early. Methods: A systematic search was conducted in Scopus, PubMed, Web of Science, and CINAHL for studies (2020-2025) applying AI to identify postpartum depression. PRISMA guidelines guided selection and appraisal. Two random-effects meta-analyses estimated pooled sensitivity and accuracy based on total sample size and reported metrics. Results: Of 1,857 records, 16 studies met inclusion criteria. Machine learning models (Random Forest, XGBoost, neural networks) showed greater accuracy than traditional methods. Integration of AI with medical records and social media data enabled earlier, personalized detection. Reported challenges included algorithmic bias, data privacy, and implementation barriers. Pooled sensitivity was 69% (95% CI: 55-81%; n=277,496) and accuracy 79% (95% CI: 73-85%; n=306,156). Conclusions: AI shows promise for enhancing postpartum depression detection and prevention but requires addressing ethical, technical, and educational challenges to achieve equitable clinical integration. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251004175, identifier CRD420251004175.
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.