Applications of artificial intelligence in point-of-care obstetric ultrasound during prenatal care: a scoping review
DOI:
https://doi.org/10.59464/2359-4632.2026.4199Keywords:
Artificial Intelligence, Prenatal Ultrasonography, Prenatal Care, Diagnostic Imaging, Scoping ReviewAbstract
Objective: To map the evidence on the use of artificial intelligence in obstetric point-of-care ultrasound during prenatal care. Methods: This scoping review was conducted according to the Joanna Briggs Institute recommendations and reported following the PRISMA-ScR checklist. Searches were performed in PubMed/MEDLINE, Scopus, and the Virtual Health Library, as well as in grey literature sources. The review question was structured using the PCC mnemonic, considering pregnant women, fetuses and/or prenatal care professionals as the population, artificial intelligence applied to obstetric ultrasound as the concept, and point-of-care obstetric ultrasound in prenatal care as the context. Results: A total of 431 records were identified in databases and 34 documents in grey literature, resulting in 17 included studies published between 2019 and 2025. Most studies focused on algorithm development, validation, or technical evaluation. The main applications involved gestational age estimation, fetal biometry, standard plane detection, fetal presentation assessment, placental location, and image quality control. Conclusion: Artificial intelligence applied to obstetric point-of-care ultrasound shows potential to expand access to prenatal imaging, support non-specialist operators, and improve obstetric screening. However, multicenter studies, external validation, and evaluation in real-world care pathways are still needed.
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