Feb 2026· Digital Health· Vol 12· 0 citations· 39 references
Medicine
TL;DR
It is suggested that AI tools can predict pregnancy complications, but a major translation gap remains—lack of external validation, interpretability, workflow integration, fairness audits, and prospective impact assessment; and a practical roadmap for developing a scalable AI-enabled maternal health solutions is proposed.
Abstract
Objective Global maternal health outcomes remain inequitable, particularly in low- and middle-income countries, due to persistent gaps in access, care continuity, and postpartum follow-up. Digital health tools, especially mobile health and artificial intelligence, offer promising avenues to enhance education, monitoring, risk stratification, and clinical decision-support. Methods This umbrella review synthesizes evidence from existing reviews on AI applications in maternal health, focusing on: (1) AI for predicting and stratifying risks of pregnancy-related complications and mortality, (2) AI-driven preventive and supportive interventions from pregnancy through postpartum, and (3) implementation barriers to real-world scale-up. It also proposes a practical roadmap for developing a scalable AI-enabled maternal health solutions.We searched PubMed, Scopus, and Web of Science for English-language reviews (2000–2025). Two reviewers independently screened records, assessed eligibility, and evaluated methodological quality using AMSTAR 2; low-quality reviews were excluded. Results Thirty-four reviews were included. Commonly employed AI methods included logistic regression, random forests, gradient boosting, support vector machines, and deep learning. Evidence indicates AI’s potential for risk prediction in hypertensive disorders, gestational diabetes, preterm birth, perinatal mental health, and other complications. However, a significant translation gap persists: external validation, model interpretability, clinical workflow integration, fairness auditing, and prospective impact evaluation were inconsistently addressed. Conclusion This umbrella review suggests that AI tools can predict pregnancy complications, but a major translation gap remains—lack of external validation, interpretability, workflow integration, fairness audits, and prospective impact assessment. Addressing these gaps is essential for equitable scale-up, especially in low- and middle-income countries. We propose a practical roadmap to guide future development from pregnancy through postpartum.
Maternal mortality and morbidity remain major global health challenges, particularly in low- and middle-income countries (LMICs), where preventable conditions such as postpartum haemorrhage, hypertensive disorders of pregnancy, and puerperal sepsis account for a substantial proportion of maternal deaths. Despite the availability of evidence-based clinical guidelines, delays in diagnosis, fragmented care pathways, and limited access to specialized services continue to compromise maternal outcomes. Digital health technologies have emerged as promising tools to address these challenges and strengthen obstetric care delivery. Electronic health records (EHRs) facilitate longitudinal patient tracking, standardized documentation, risk stratification, and improved coordination across the continuum of maternal care. Clinical decision support systems (CDSS) provide real-time, guideline-based recommendations that enhance clinical decision-making during obstetric emergencies and reduce variability in care. Furthermore, advances in artificial intelligence (AI) and machine learning enable early prediction of high-risk conditions such as preeclampsia, gestational diabetes, and preterm birth, allowing timely interventions and more efficient allocation of healthcare resources. Telemedicine has expanded access to specialist consultations and follow-up services, particularly for women in rural settings, thereby improving continuity of care and reducing geographical barriers. The synergistic integration of these technologies creates a comprehensive digital ecosystem capable of enhancing maternal risk assessment, clinical management, and healthcare accessibility. However, challenges related to data privacy, interoperability, digital infrastructure, workforce training, must be addressed to ensure equitable implementation. Strategic investment in digital health infrastructure and governance frameworks can facilitate the effective adoption of these innovations, ultimately contributing to reduced maternal morbidity and mortality and improved maternal health.
Amrita Singh, Medha Davile, S. Mundle et al.· Journal of Medico Informatic...· 0 citations
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.
B. Abdi, Shadia Mohamed Ali, Sumeya Ahmed Ali et al.· Frontiers in Public Health· 0 citations
Abstract 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.
Shahrzad Kaveh, Zahra Khedri, Solmaz Sohrabei· Oxford Open Digital Health· 0 citations
This first scoping review of AI applications in hypertension health education identified a mismatch between rapid advances in generative AI and the limited availability of rigorous clinical evidence.
Haoran Chen, Shenglan Xiao, Tong Wan et al.· Journal of Medical Internet...· 0 citations
Global health inequities continue to restrict access to timely and quality care in low- income settings Which have limited infrastructure, workforce shortages, financial barriers & geographic disparities that remain persistent challenges. Emerging advancements in artificial intelligence, particularly agentic AI, offer new opportunities to strengthen healthcare delivery in the resource constrained environments. These systems can support in decision making, improve early diagnosis, and enhance service reach.
This chapter adopts a narrative literature review and conceptual analysis. It synthesises the evidence from peer reviewed studies, global health reports, and the policy documents. The analysis examines applications of agentic AI in areas such as telehealth, disease surveillance, clinical support and the community based care. It also evaluates critical concerns including digital exclusion, algorithmic bias ,data privacy, and infrastructural limitations.
The chapter argues that agentic the AI can help in reducing healthcare disparities when implemented within inclusive and ethically governed frameworks. It also highlights the need for context-sensitive design, local capacity development & the supportive policy environments. The study also provides the structured understanding of how AI-driven solutions can help in improving accessibility, efficiency and equity in the underserved populations.
D. Bali, Prithviraj Banerjee· International journal of res...· 0 citations
Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. However, most studies focus on model development and predictive performance, with less attention to the health-system processes required to generate ML-ready data and translate risk information into clinical action. The Mlinde Mama Project in Tanzania combined Group Antenatal Care (G-ANC), digital maternal health systems, and development of an ML-enabled risk stratification model for hypertensive disorders of pregnancy (HDP). This study examined the health-system conditions shaping the pathway from routine care to actionable ML-enabled risk information. Methods We conducted a retrospective mixed-methods implementation analysis of this project that was implemented between 2022 and 2024 in Geita, Tanzania. The analysis triangulated endline evaluation findings, quantitative exit interviews with pregnant women, focus group discussions with women and healthcare workers, key informant interviews, project implementation records, routine data from Tanzania's Unified Community System (UCS), and documented ML development experience. Relevant quotations from the final evaluation report were systematically screened, selected, and coded. An abductive analysis combined inductively derived themes with the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework and a study-specific ML implementation pathway. Results Seven themes emerged across three phases of the implementation pathway. ML readiness began before the algorithm, with reliable clinical measurement and documentation; patient participation and task-sharing redistributed data generation. The paper-to-digital transition shaped which information became ML-ready data. The potential value of ML depended on workflow redesign rather than prediction alone. Technology created an efficiency paradox in which task-sharing reduced workload while staffing shortages and confirmatory work generated new burdens. Advanced analytics depended on basic infrastructure, while sustainability relied on teamwork, trust, user demand, and local ownership. Conclusions Our findings suggest that successful ML implementation in maternal health begins with health-system readiness before the algorithm itself. A critical paper-to-digital transition stage was a major determinant of data quality and ML readiness. ML-enabled maternal risk stratification should therefore be approached as a sociotechnical intervention spanning measurement, digitization, clinical confirmation, and follow-up. This implementation readiness is critical for the successful development of an ML-enabled risk stratification system. Crucially, our findings resonate with the key domains of the NASSS framework, underscoring how addressing multidimensional complexities, from technological design to organizational readiness, is vital for successful scale-up and long-term sustainability.
A. Hellar, I. Lyatuu, A. Kinyina et al.· medRxiv· 0 citations
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