# Artificial intelligence in the prevention and early detection of postpartum depression: a systematic review and meta-analysis

## TL;DR

This paper reviews how artificial intelligence can help detect and prevent postpartum depression, finding that AI models are more accurate than traditional methods but face challenges like bias and privacy issues.

## Contribution

The study is the first systematic review and meta-analysis evaluating AI's role in postpartum depression prevention and early detection.

## Key findings

- Machine learning models outperformed traditional methods in detecting postpartum depression.
- AI integration with medical records and social media data enabled earlier and personalized detection.
- Pooled sensitivity and accuracy of AI models were 69% and 79%, respectively.

## Abstract

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.

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.

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).

AI shows promise for enhancing postpartum depression detection and prevention but requires addressing ethical, technical, and educational challenges to achieve equitable clinical integration.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251004175, identifier CRD420251004175.