Keywords
Artificial intelligence
Obstetrics
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Abstract
Objective: To analyze and synthesize the scientific evidence available in the literature on the methods of artificial intelligence (AI) in the early prediction and diagnosis of pregnancy complications. Methods: This is an integrative review, conducted on the EMBASE, PubMed, Scopus, and Web of Science databases, using the following descriptors: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Pregnancy Complications”, “perinatal complications” and “Postpartum”. Studies published between 2019 and 2023 that use AI methods to predict pregnancy complications were included. Results: The final sample consisted of 13 articles, with a higher concentration of publications in the years 2019 and 2020. Most studies were conducted in China, and the predominant research designs were cohort studies. The AI models used were effective in predicting complications such as preeclampsia, gestational diabetes mellitus, premature birth, and placental invasion, using electronic medical records and other biomarkers. Final considerations: This review highlights the significant impact of AI models in improving the prediction and diagnosis of pregnancy complications. The results underline the need for continuous integration of these technologies in obstetric clinical practice, as well as future research to validate and expand their applicability in different clinical contexts.
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