{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T15:00:03Z","timestamp":1767193203644,"version":"3.48.0"},"reference-count":25,"publisher":"BMJ","issue":"8","license":[{"start":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T00:00:00Z","timestamp":1755993600000},"content-version":"unspecified","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2025,8,8]]},"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Maternal mortality remains a critical public health challenge in low- and middle-income countries (LMICs), where over 92% of global maternal deaths occur. Artificial intelligence (AI)-enabled solutions are increasingly recognised for their potential to improve and expand health services delivered to women. Such solutions can accelerate how health systems address gaps in maternal healthcare, including prevention, early detection, intervention and treatment. However, the extent to which AI-enabled solutions have progressed toward real-world application in LMIC healthcare systems remains unclear. This scoping review aims to systematically map the development of AI-enabled solutions for maternal health by applying the Technology Readiness Level (TRL) framework to assess their stage of advancement. It also aims to identify facilitators, barriers and critical research gaps.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>This scoping review will be guided by established methodological frameworks and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. A comprehensive literature search will be performed across PubMed, EMBASE, IEEE Xplore, CINAHL, CABI and Scopus, as well as grey literature sources. The search will combine controlled vocabulary and keywords related to \u2018artificial intelligence\u2019 and \u2018maternal health\u2019. Studies reporting any AI-enabled solutions in maternal health, specifically targeting the leading direct causes of maternal mortality (eg, postpartum haemorrhage, hypertensive disorders, sepsis, delivery complications and unsafe abortion), published between 1 January 2015 and 1 August 2025, will be eligible. Two independent reviewers will screen studies, chart relevant data and resolve discrepancies through consensus. Findings will be synthesised using a narrative and tabular approach to map the extent and characteristics of the literature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>Ethical approval is not required as the review involves analysis of publicly available data. The findings will be disseminated through publication in a peer-reviewed journal and presentations at relevant conferences.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2025-105622","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T01:45:41Z","timestamp":1756086341000},"page":"e105622","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["From pilot to practice: a scoping review protocol mapping the development of AI-enabled solutions for maternal health using technology readiness levels"],"prefix":"10.1136","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7363-2055","authenticated-orcid":false,"given":"Nico","family":"Marquardt","sequence":"first","affiliation":[{"name":"Institute of Public Health, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Berlin, Germany"},{"name":"Digital Global Public Health, Hasso-Plattner-Institut, University Potsdam, Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir","family":"Choi","sequence":"additional","affiliation":[{"name":"Free University of Berlin, Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles","family":"Martyn-Dickens","sequence":"additional","affiliation":[{"name":"Komfo Anokye Teaching Hospital, Kumasi, Ghana"},{"name":"Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marelize","family":"Gorgens","sequence":"additional","affiliation":[{"name":"World Bank, Washington, District of Columbia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sam","family":"Mathewlynn","sequence":"additional","affiliation":[{"name":"Nuffield Dept. of Women\u2019s & Reproductive Health, University of Oxford, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7169-2620","authenticated-orcid":false,"given":"Tobias","family":"Kurth","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Bouteiller","sequence":"additional","affiliation":[{"name":"XU Exponential University of Applied Sciences, Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lothar H","family":"Wieler","sequence":"additional","affiliation":[{"name":"Digital Global Public Health, Hasso-Plattner-Institut, University Potsdam, Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2025,8,24]]},"reference":[{"key":"2025123106473865000_15.8.e105622.1","unstructured":"World Health Organization . Maternal mortality. 2025. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/maternal-mortality [Accessed 20 May 2025]."},{"key":"2025123106473865000_15.8.e105622.2","unstructured":"WHO . Trends in maternal mortality estimates 2000 to 2023. 2025. Available: https:\/\/iris.who.int\/bitstream\/handle\/10665\/381012\/9789240108462-eng.pdf [Accessed 20 May 2025]."},{"key":"2025123106473865000_15.8.e105622.3","unstructured":"WHO . Seventy-seventh world health assembly a77\/a\/conf.\/5 agenda item 11.7 accelerate progress towards reducing maternal, newborn and child mortality in order to achieve sustainable development goal targets 3.1 and 3.2. 2024. Available: https:\/\/apps.who.int\/gb\/ebwha\/pdf_files\/WHA77\/A77_ACONF5-en.pdf [Accessed 20 May 2025]."},{"key":"2025123106473865000_15.8.e105622.4","doi-asserted-by":"crossref","unstructured":"Silva Rocha E da , de Morais Melo FL , de Mello MEF , et al . On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. 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Artificial intelligence for improved maternal healthcare: a systematic literature review. 2023 IEEE AFRICON; Nairobi, Kenya, 2023:1\u20136. doi:10.1109\/AFRICON55910.2023.10293674","DOI":"10.1109\/AFRICON55910.2023.10293674"},{"key":"2025123106473865000_15.8.e105622.12","doi-asserted-by":"crossref","unstructured":"Mapari SA , Shrivastava D , Dave A , et al . Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility. Cureus 2024;16. doi:10.7759\/cureus.69555","DOI":"10.7759\/cureus.69555"},{"key":"2025123106473865000_15.8.e105622.13","doi-asserted-by":"publisher","DOI":"10.1080\/1364557032000119616"},{"key":"2025123106473865000_15.8.e105622.14","doi-asserted-by":"publisher","DOI":"10.7326\/M18-0850"},{"key":"2025123106473865000_15.8.e105622.15","doi-asserted-by":"publisher","DOI":"10.1097\/XEB.0000000000000277"},{"key":"2025123106473865000_15.8.e105622.16","unstructured":"Kimmel WM , Beauchamp PM , Frerking MA , et al . Technology readiness assessment best practices guide. ntrsnasagov; 2020. Available: https:\/\/ntrs.nasa.gov\/citations\/20205003605 [Accessed 20 May 2025]."},{"key":"2025123106473865000_15.8.e105622.17","unstructured":"European Commission . Technology readiness levels (trl). 2014. 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Guidelines for snowballing in systematic literature studies and a replication in software engineering. EASE \u201914; London England United Kingdom, May 13, 2014:2014. doi:10.1145\/2601248.2601268 Available: https:\/\/dl.acm.org\/doi\/proceedings\/10.1145\/2601248","DOI":"10.1145\/2601248.2601268"},{"key":"2025123106473865000_15.8.e105622.23","doi-asserted-by":"crossref","DOI":"10.1186\/s13643-016-0384-4","article-title":"Rayyan-a web and mobile app for systematic reviews","volume":"5","author":"Ouzzani","year":"2016","journal-title":"Syst Rev"},{"key":"2025123106473865000_15.8.e105622.24","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.g7647","article-title":"Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation","volume":"350","author":"Shamseer","year":"2015","journal-title":"BMJ"},{"key":"2025123106473865000_15.8.e105622.25","unstructured":"Mankins JC . Technology readiness levels - a white paper. 1995. 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