Artificial Intelligence for Sexual, Reproductive and Maternal Health: A Scoping Review (Preprint)

Abstract

<sec> <title>BACKGROUND</title> - </sec> <sec> <title>OBJECTIVE</title> To survey and analyze the applications of artificial intelligence tools in the field of sexual, reproductive and maternal health in Latin America and the Caribbean. </sec> <sec> <title>METHODS</title> A scoping review was conducted using the methodological framework proposed by Arksey and O'Malley and the PRISMA statement for Systematic Reviews and Meta-analyses, extension for Scoping Reviews. The search was carried out in PubMed, Scielo, Cochrane and Lilacs. </sec> <sec> <title>RESULTS</title> Of the 1,518 articles identified, 143 were included and analyzed. The review evidences the predominant focus of artificial intelligence for prenatal, childbirth, and postnatal care, as well as in the prevention, detection, and treatment of cancers of the reproductive organs. The tools mainly use machine learning and deep learning techniques, and are in different stages of development: 48% represent exploratory projects, 17% are operational tools but do not have a results report, and 35% are projects implemented in real contexts and provide evidence of the effectiveness of the tools used. </sec> <sec> <title>CONCLUSIONS</title> The need to diversify the applications of artificial intelligence in essential sexual, reproductive and maternal health services and ensure the inclusion of diverse populations to ensure equitable and effective access to them is highlighted </sec> <sec> <title>CLINICALTRIAL</title> <p/> </sec>

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