{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:59:47Z","timestamp":1790315987253,"version":"4.1.0"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>Postpartum depression (PPD) affects 10\u201320% of new mothers globally, yet detection and treatment rates remain low, particularly in underserved populations. Artificial intelligence (AI)\u2014here spanning supervised machine-learning prediction, natural-language and acoustic analysis, and generative conversational agents\u2014is increasingly promoted as a solution, from risk prediction to chatbot-delivered therapy. Drawing on selected evidence from clinical informatics, digital mental health, public-health ethics, and implementation science, we argue that although AI may genuinely expand reach, its current trajectory, driven by technical feasibility rather than public-health value, could widen rather than narrow maternal mental-health inequities. This risk plays out across four domains: (1) algorithmic accuracy that does not translate into population outcomes; (2) training data that structurally exclude marginalized groups; (3) algorithmic care that may erode relational support; and (4) a regulatory vacuum that permits deployment without accountability. Distinguishing published evidence from our own recommendations, we conclude that AI for PPD prevention should be governed as a public good through equity-centered design, real-world evidence standards (including calibration and subgroup performance beyond AUC), tiered device oversight, layered data consent, and clearly specified safety pathways. We call for a global dialogue among policymakers, developers, health systems, researchers, and women with lived experience to operationalize these principles before large-scale deployment.<\/jats:p>","DOI":"10.3389\/fpubh.2026.1967813","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:31:43Z","timestamp":1790314303000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Putting equity first: reorienting artificial intelligence for population-level postpartum depression prevention"],"prefix":"10.3389","volume":"14","author":[{"given":"Ya-li","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Gynecology, Suqian Hospital of Nanjing Drum Tower Hospital Group","place":["Suqian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian-qian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Suqian Hospital of Nanjing Drum Tower Hospital Group","place":["Suqian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics, Suqian Hospital of Nanjing Drum Tower Hospital Group","place":["Suqian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1146\/annurev-med-050423-095712","article-title":"Transformative therapies for depression: postpartum depression, major depressive disorder, and treatment-resistant depression","volume":"76","author":"Richardson","year":"2025","journal-title":"Annu Rev Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e32745","DOI":"10.7759\/cureus.32745","article-title":"A comprehensive review on postpartum 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