THE ROLE OF ARTIFICIAL INTELLIGENCE IN MATERNAL HEALTHCARE: ENHANCING MIDWIFERY PRACTICES TO REDUCE HEALTH DISPARITIES, A SYSTEMATIC REVIEW

Abstract

Background: Artificial Intelligence (AI) is transforming midwifery by enhancing clinical decision-making, improving maternal and neonatal outcomes, and optimizing healthcare efficiency. AI-driven technologies such as predictive analytics and decision-support systems help midwives identify high-risk pregnancies, monitor fetal health, and automate administrative tasks. Despite its potential, AI integration presents challenges related to ethical considerations, data privacy, and the risk of reduced human interaction in maternity care. The need for AI education in midwifery training is essential for responsible and effective implementation.

Objective: This review examines the role of AI in midwifery by synthesizing existing literature on its applications, benefits, and challenges. It also explores the necessity of integrating AI education into midwifery curricula to prepare future professionals for evolving technological advancements.

Methods: A systematic literature review was conducted following PRISMA guidelines. Databases including PubMed, CINAHL, Google Scholar, and Scopus were searched using keywords such as "Artificial Intelligence," "Midwifery," "Predictive Analytics," and "Maternal Healthcare." Studies published in English within the last five years were included. Peer-reviewed articles, systematic reviews, and clinical trials discussing AI applications, ethical concerns, and midwifery education were analyzed. A total of 225 articles were initially identified, with 19 studies meeting the final inclusion criteria.

Results: AI-driven predictive models significantly improved early detection of preeclampsia, postpartum hemorrhage, and fetal distress, reducing maternal complications by 30-40%. Decision-support systems enhanced diagnostic accuracy by 25%, reducing human error. AI-driven administrative automation decreased midwives' documentation workload by 40%, allowing increased patient engagement. Virtual assistants and chatbots improved maternal education and access to care by 50%, particularly in underserved regions. Despite these benefits, concerns regarding algorithmic bias (reported in 20% of studies), data privacy risks (identified in 35% of studies), and the potential loss of human-centered care remain critical barriers to AI adoption in midwifery.

Conclusion: AI has the potential to revolutionize midwifery by improving clinical efficiency, reducing complications, and enhancing patient education. However, addressing ethical, legal, and technical challenges is essential for its responsible implementation. Integrating AI education into midwifery training is crucial to ensure that midwives are equipped with the necessary skills to navigate AI-driven healthcare environments. Future research should focus on ethical frameworks, policy development, and AI literacy among midwives to facilitate equitable AI adoption in maternal healthcare.