{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:28:38Z","timestamp":1787070518907,"version":"build-2736575974"},"reference-count":45,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":215,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The convergence of artificial intelligence (AI) and wearable monitoring devices is revolutionizing the management of high-risk pregnancies, offering unprecedented opportunities for early detection, personalized care, and improved outcomes. These technologies address critical challenges in maternal and fetal health, especially in resource-limited settings, by enabling proactive and continuous care. This systematic review specifically focuses on the integration of artificial intelligence and wearable devices for managing high-risk pregnancies and highlights regulatory and ethical challenges. Databases like PubMed (Medline), Scopus, and Web of Science were explored for pertinent studies (from 2015 to 2024). Articles were chosen based on specific inclusion and exclusion criteria, and essential data including technology type, AI algorithms employed, and their influence on perinatal care were collected. The Risk of Bias in the studies was evaluated utilizing standard assessment tools such as PROBAST+AI, and study quality was assessed with TRIPOD+AI. Ultimately, the findings were examined through narrative analysis, leading to the identification of challenges and future research directions. This systematic review examined 16 studies focusing on the application of AI and digital innovations in maternal and fetal healthcare. The studies investigated various AI techniques, including machine-learning models for predicting risks to maternal health, deep learning for monitoring fetal well-being, and wearable sensor technologies for remote health tracking. Many studies produced encouraging outcomes, with AI models reaching high levels of accuracy in early detection, risk assessment, and real-time patient monitoring. However, issues such as dataset biases, generalizability, and ethical concerns continue to pose significant challenges. Additional research is necessary to validate these AI-driven methods across diverse populations to ensure equitable and effective healthcare outcomes. The integration of technologies has the potential to significantly improve the management of high-risk pregnancies. However, a critical regulatory gap exists that must be addressed to ensure that these technologies are used ethically, safely, and effectively. This will involve the development of new regulatory frameworks, a focus on transparency and accountability, and a commitment to addressing bias and data security to provide the best care possible. AI has the potential to transform maternity services by improving risk prediction, enabling remote monitoring, and supporting clinical decisions. However, realizing this potential requires addressing limitations related to data quality, algorithm accuracy, user privacy, and ethical considerations.<\/jats:p>","DOI":"10.1093\/oodh\/oqag020","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T11:48:19Z","timestamp":1785498499000},"source":"Crossref","is-referenced-by-count":0,"title":["Transforming perinatal health with AI-predictive models in precision and digital innovations for maternal and fetal well-being: a systematic review"],"prefix":"10.1093","volume":"4","author":[{"given":"Shahrzad","family":"Kaveh","sequence":"first","affiliation":[{"name":"Department of Midwifery, School of Nursing and Midwifery, Ayatollah Mousavi Hospital, Zanjan University of Medical Sciences , Sabooti Street 4513956184, Zanjan ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Khedri","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Ziaeian Hospital, Tehran University of Medical Sciences , Qazvin Street 1366736511, Tehran ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0736-5278","authenticated-orcid":false,"given":"Solmaz","family":"Sohrabei","sequence":"additional","affiliation":[{"name":"Student Research Committee (Department and Faculty of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences), Shahid Beheshti University of Medical Sciences , Darband Street 197653213, Tehran ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"2026081811270893700_ref1","doi-asserted-by":"publisher","DOI":"10.48001\/JoCEVD","article-title":"Advancements in AI-powered personalized pregnancy care: a comprehensive review","volume":"2","author":"Takale","year":"2024","journal-title":"J Commun Eng VLSI Design"},{"key":"2026081811270893700_ref2","doi-asserted-by":"publisher","DOI":"10.1177\/17455065211018111","article-title":"Artificial intelligence: a rapid case for advancement in the personalization of gynaecology\/obstetric and mental health care","volume":"17","author":"Delanerolle","year":"2021","journal-title":"Womens Health"},{"key":"2026081811270893700_ref3","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1161\/ATVBAHA.124.321673","article-title":"Artificial intelligence and machine learning in preeclampsia","volume":"45","author":"Layton","year":"2025","journal-title":"Arterioscler Thromb Vasc Biol"},{"key":"2026081811270893700_ref4","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1016\/j.molmed.2021.01.007","article-title":"Data-driven modeling of pregnancy-related complications","volume":"27","author":"Espinosa","year":"2021","journal-title":"Trends Mol Med"},{"key":"2026081811270893700_ref5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.25163\/angiotherapy.889905","article-title":"Machine learning models for predicting risky pregnancies in early clinical interventions","volume":"8","author":"Hasan","year":"2024","journal-title":"J Angiother"},{"key":"2026081811270893700_ref6","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12978-024-01839-5","article-title":"Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol","volume":"21","author":"Shaban","year":"2024","journal-title":"Reprod Health"},{"key":"2026081811270893700_ref7","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.3390\/healthcare10112164","article-title":"Application of artificial intelligence in screening for adverse perinatal outcomes\u2014a systematic review","volume":"10","author":"Feduniw","year":"2022","journal-title":"InHealthcare"},{"key":"2026081811270893700_ref8","doi-asserted-by":"publisher","first-page":"1007799","DOI":"10.3389\/fped.2022.1007799","article-title":"Fetal-maternal monitoring in the age of artificial intelligence and computer-aided decision support: a multidisciplinary perspective","volume":"10","author":"Georgieva","year":"2022","journal-title":"Front Pediatr"},{"key":"2026081811270893700_ref9","doi-asserted-by":"publisher","first-page":"108682","DOI":"10.1016\/j.cmpb.2025.108682","article-title":"Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: unveiling transformative capabilities and challenges","volume":"263","author":"Tadepalli","year":"2025","journal-title":"Comput Methods Prog Biomed"},{"key":"2026081811270893700_ref10","doi-asserted-by":"publisher","first-page":"903","DOI":"10.2147\/IJWH.S454127","article-title":"A theoretical exploration of artificial intelligence\u2019s impact on feto-maternal health from conception to delivery. 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