Marquardt Nico, Choi Vladimir, Martyn-Dickens Charles, Gorgens Marelize, Mathewlynn Sam, Kurth Tobias, Bouteiller Philipp, Wieler Lothar H
Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
Digital Global Public Health, Hasso-Plattner-Institut, University Potsdam, Potsdam, Germany.
BMJ Open. 2025 Aug 24;15(8):e105622. doi: 10.1136/bmjopen-2025-105622.
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.
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 'artificial intelligence' and 'maternal health'. 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.
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.
孕产妇死亡率仍是低收入和中等收入国家(LMICs)面临的一项严峻公共卫生挑战,全球超过92%的孕产妇死亡发生在这些国家。基于人工智能(AI)的解决方案因其改善和扩大为女性提供的医疗服务的潜力而日益受到认可。此类解决方案可加快卫生系统解决孕产妇保健差距的速度,包括预防、早期检测、干预和治疗。然而,基于人工智能的解决方案在LMICs卫生系统中向实际应用推进的程度仍不明确。本范围综述旨在通过应用技术就绪水平(TRL)框架系统地梳理基于人工智能的孕产妇健康解决方案的发展情况,以评估其进展阶段。它还旨在确定促进因素、障碍和关键研究差距。
本范围综述将以既定的方法框架为指导,并符合系统评价和Meta分析扩展版的首选报告项目(PRISMA-ScR)。将在PubMed、EMBASE、IEEE Xplore、CINAHL、CABI和Scopus以及灰色文献来源中进行全面的文献检索。检索将结合与“人工智能”和“孕产妇健康”相关的控制词汇和关键词。2015年1月1日至2025年8月1日期间发表的报告任何基于人工智能的孕产妇健康解决方案、专门针对孕产妇死亡的主要直接原因(如产后出血、高血压疾病、败血症、分娩并发症和不安全堕胎)的研究将符合纳入标准。两名独立评审员将筛选研究、绘制相关数据并通过协商一致解决差异。将采用叙述性和表格性方法综合研究结果,以梳理文献的范围和特征。
由于本综述涉及对公开可用数据的分析,因此无需伦理批准。研究结果将通过在同行评审期刊上发表以及在相关会议上进行报告来传播。