Transforming Maternal Health: A Systematic Review of Holistic Digital and AI-Based Solutions for Maternal Health

Document Type

Article

Abstract

Maternal mortality and morbidity are the main causes for treatable deaths and disability of child-bearing age worldwide, with extreme difficulties seen in low- and middle-income countries (LMICs). Reasons for maternal health outcomes are mainly influenced by correlated factors such as clinical and social factors, including chronic health conditions, mental health issues, socioeconomic barriers, geography, pregnancy stages, and maternal age. This review assessed the feasibility, efficacy, and acceptability of the AI-based maternal health interventions over the maternal continuum of care in all stages of pregnancy. It followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for each step, and an extensive search was performed in March 2026 in 5 bibliographic databases, and further identified 26 relevant articles. Three major themes were found. Theme 1: Earlier mobile health applications and rule-based chatbots have shown greater use and higher user adoption, mainly when they are coordinated with community health workers. Theme 2: Single-agent AI systems and machine learning models both have indicated strong internal performance in terms of mental health evaluation and risk prediction metrics, but these results were based only on local datasets and validated with internal verification. Theme 3: Even with such emerging technologies, there were no multi-agent or agentic AI systems recognized in the studies, highlighting equality problems and coordination gaps in maternal care delivery. This literature review indicates that AI-based maternal care interventions exhibit potential capacities to help pregnant women in all stages and identify associated risks in advance, mainly in limited resource regions, but only when coordinated with human guidance. This study also highlights the need for targeting rigorous trials, equality-focused execution, and the invention of combining AI approaches that could handle complicated causes of maternal health. To conclude, digital and AI-driven maternal health can show potential to support women throughout all pregnancy stages and improve early risk notification, especially in limited-resource regions. Hence, their impact mainly relies on human supervision, equality-based, and system-level coordination.

Digital Object Identifier (DOI)

https://doi.org/10.1109/ichi69079.2026.00153

APA Citation

Markanti, R., Wu, D., & De Fede, A. L. (2026). Transforming maternal health: A systematic review of holistic digital and AI-Based solutions for maternal health. 2026 IEEE 14th International Conference on Healthcare Informatics (ICHI), 1194–1196. https://doi.org/10.1109/ichi69079.2026.00153

Link to Record

Share

COinS