Managing maternity: Moving care, not patients, using artificial intelligence ( AI ), internet‐of‐things ( IOT ) and point‐of‐care testing ( POCT ) devices

Journal article

The integration of artificial intelligence (AI) into healthcare is accelerating and maternity care is at a pivotal moment for the strategic implementation of these technologies. This article explores how AI‐assisted women's health innovations, often termed “FemTech,” may transform pregnancy care by addressing long‐standing disparities: enhancing diagnostic precision and supporting the obstetric workforce. We outline three domains in which AI is poised to drive change: where women are cared for, how they are cared for, and who delivers their care. First, decentralized AI combined with Internet of Medical Things (IoMT) devices can extend prenatal monitoring into homes, reducing reliance on clinic visits and expanding access for underserved populations. Second, predictive and reinforcement learning algorithms enable personalized, adaptive care across the reproductive continuum, from preconception to postpartum, moving beyond static risk models and uniform treatment approaches. Third, AI has the potential to augment the maternity workforce by offering generative tools for patient engagement, clinical decision support and automation of ultrasound imaging, while ensuring clinician oversight remains central. Future adoption will depend on global economic and geopolitical dynamics, with the USA and China currently leading in patents, publications, and model development. Equitable integration will require explainable AI, transparent validation, multinational benchmark datasets, and robust governance on safety and consent. Ultimately, AI‐powered technologies should complement, not replace human expertise, embedding digital innovation within a model of maternity care that preserves empathy and clinical judgment.

Authors: Foo, L; Choolani, M; Papageorghiou, A; Divakar, H; Shehata, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: International Journal of Gynecology & Obstetrics
• Article number: ijgo.70942
• Publication date: 2026-03-17
• Acceptance date: 2026-02-23
• DOI: 10.1002/ijgo.70942
• EISSN: 1879-3479
• ISSN: 0020-7292
• Language: English
• Keywords: maternity care; FemTech; large language models; artificial intelligence; machine learning; digital health; hybrid clinics; deep learning