{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T12:23:05Z","timestamp":1787660585555,"version":"build-2736575974"},"reference-count":93,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"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>Maternal and newborn mortality remain major public health challenges in low-income countries, where poverty, health workforce shortages, limited access to quality care, and weak health systems contribute to preventable deaths and adverse outcomes. Artificial intelligence (AI) has emerged as a promising tool for strengthening maternal and newborn health through improved risk prediction, early diagnosis, clinical decision support, and health system planning. This narrative review examines the potential of AI to advance equity, enhance early detection of complications, and improve health system resilience in resource-constrained settings. Evidence suggests that AI applications can support the identification of high-risk pregnancies, improve the early detection of conditions such as preeclampsia, neonatal sepsis, and respiratory disorders, expand access to obstetric ultrasound services, and optimize resource allocation. AI-enabled digital health platforms also have the potential to strengthen community outreach, referral systems, and quality improvement initiatives across the continuum of maternal and newborn care. However, significant challenges remain, including inadequate digital infrastructure, limited technical capacity, poor data quality, algorithmic bias, and weak regulatory frameworks. The review concludes that AI can contribute meaningfully to reducing maternal and neonatal morbidity and mortality when integrated within broader health system strengthening efforts. Strategic investments in governance, workforce development, digital infrastructure, and equity-focused implementation are essential to ensure that AI technologies support sustainable and inclusive improvements in maternal and newborn health outcomes.<\/jats:p>","DOI":"10.3389\/fpubh.2026.1908521","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T11:36:40Z","timestamp":1787657800000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence and the future of maternal and newborn health in low-income countries: advancing equity, early detection, and health system resilience"],"prefix":"10.3389","volume":"14","author":[{"given":"Bashir Mohamed","family":"Abdi","sequence":"first","affiliation":[{"name":"Faculty of Health Sciences and Tropical Medicine, Somali National University","place":["Mogadishu, Somalia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shadia Mohamed","family":"Ali","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences and Tropical Medicine, Somali National University","place":["Mogadishu, Somalia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumeya Ahmed","family":"Ali","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences and Tropical Medicine, Somali National University","place":["Mogadishu, Somalia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed Abdikadir","family":"Hussein","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences and Tropical Medicine, Somali National University","place":["Mogadishu, Somalia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulkadir Ibrahim","family":"Adow","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences and Tropical Medicine, Somali National University","place":["Mogadishu, Somalia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharmake Gaiye","family":"Bashir","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences and Tropical Medicine, Somali National University","place":["Mogadishu, Somalia"]},{"name":"Center for Health Research and Innovation, Somali National University","place":["Mogadishu, Somalia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1111\/BIRT.70037","article-title":"Next-gen midwifery support: designing an artificial intelligence (AI) enhanced Mobile app for pregnancy risk categorization and clinical decision support on maternal and neonatal 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