SynthesisBMJ mental health2025
Leveraging artificial intelligence in the prediction, diagnosis and treatment of depression and anxiety among perinatal women in low- and middle-income countries: a systematic review.
Synthesis in BMJ mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging artificial intelligence in the prediction, diagnosis and treatment of depression and anxiety among perinatal women in low- and middle-income countries: a systematic review.BMJ mental health · 2025Pooled it
- Advances in Perinatal Depression: A Focus on Screening and Treatment.Journal of clinical medicine · 2026Review
- Article
- Service use and obstetric outcomes in pregnant women attending a Women's Mental Health Clinic.Journal of the colleges of medicine of South Africa · 2026Article
- Mental health chatbots and their technical features: A systematic review of reviews and a thematic analysis.Global mental health (Cambridge, England) · 2026Review
- Perinatal depression and anxiety in Ghana: a qualitative study of women's perspectives on AI-driven interventions.BMC pregnancy and childbirth · 2025Article
- Filling the gap: artificial intelligence-driven one health integration to strengthen pandemic preparedness in resource-limited settings.Frontiers in public health · 2025Review
- Role of Chatbots on Supporting Women in Perinatal Period: A Scoping Review.Women's health reports (New Rochelle, N.Y.)Article
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
aimThe adoption of artificial intelligence (AI) tools is gaining traction in maternal mental health (MMH) research. Despite its growing usage, little is known about its prospects and challenges in low- and middle-income countries (LMICs). This study aims to systematically review articles on the role of AI in addressing MMH in LMICs.
methodsThis systematic review adopts a patient and public involvement approach to investigate the role of AI in predicting, diagnosing or treating perinatal depression and anxiety (PDA) among perinatal women in LMICs. Seven databases were searched for studies that reported on AI tools/methods for PDA published between January 2010 and July 2024. Eligible studies were identified and extracted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines using Covidence, and the data were synthesised using thematic analysis.
resultsOut of 2203 studies, 19 studies across eight countries were deemed eligible for extraction and synthesis. The review revealed that the supervised machine learning method was the most common AI approach and was used to improve the early detection of depression and anxiety among perinatal women. Additionally, postpartum depression was the most frequently investigated MMH condition in this study. Further, the review revealed only three conversational agents (CAs)/chatbots used to deliver psychological treatment.
conclusionsThe findings underscore the potential of AI-based methods in identifying risk factors and delivering psychological treatment for PDA. Future research should investigate the underlying mechanisms of the effectiveness of AI-based chatbots/CAs and assess the long-term effects for diagnosed mothers, to aid the improvement of MMH in LMICs. PROSPERO REGISTRATION NUMBER: CRD42024549455.
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.