APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN OBSTETRIC ULTRASONOGRAPHY: ADVANCES, BENEFITS, AND CHALLENGES
DOI:
https://doi.org/10.56238/levv16n51-050Keywords:
Artificial Intelligence, Obstetric Ultrasonography, Prenatal Care, Diagnostic Imaging, Maternal and Fetal HealthAbstract
Obstetric ultrasonography is recognized as an essential tool in prenatal care, allowing early identification of conditions that may affect maternal and fetal health. However, the traditional method presents limitations such as operator-dependent variability, prolonged examination time, and difficulties in access in regions with limited infrastructure. In this context, artificial intelligence has been incorporated as a strategy to optimize diagnostic processes, reduce examination time, and increase the reproducibility of results. This study aims to critically analyze the applications of artificial intelligence in obstetric ultrasonography, highlighting recent advances, perceived clinical benefits, risks, and implementation challenges. The deductive method was adopted, with bibliographic research in specialized sources. The results indicate that although the technology offers important operational and diagnostic gains, its full adoption depends on overcoming technical, ethical, financial, and regulatory barriers. It is concluded that the incorporation of artificial intelligence in obstetric ultrasonography requires strategic planning, professional training, and specific regulation.
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References
BACKES, D. S. et al. Pré-natal coletivo mediado por tecnologia educativa: percepção de gestantes. Ciência & Saúde Coletiva, v. 29, n. 1, p. e00392023, 2024.
BASHIR, Z. et al. Validação clínica de IA explicável para exames de crescimento fetal por meio de avaliação prospectiva multinível e interinstitucional do usuário final. Scientific Reports, v. 15, 2074, 2025. Disponível em: https://doi.org/10.1038/s41598-025-86536-4. Acesso em: 20 jun. 2025.
BORBOREMA, R. D. B. et al. Technological advances in obstetric nursing consultations using ultrasound. Texto & Contexto – Enfermagem, v. 33, p. e20230236, 2024.
CHEN, Z. et al. Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications. Frontiers in Medicine, v. 8, 733468, 2021. DOI: 10.3389/fmed.2021.733468.
DELPINO, F. M. et al. Emergency department use and Artificial Intelligence in Pelotas: design and baseline results. Revista Brasileira de Epidemiologia, v. 26, e230021, 2023. Disponível em: https://doi.org/10.1590/1980-549720230021. Acesso em: 20 jun. 2025.
GINSBURG, A. S. et al. Um levantamento dos usos e prioridades da ultrassonografia obstétrica assistida por inteligência artificial em países de baixa e média renda. Scientific Reports, v. 15, 3873, 2025. Disponível em: https://doi.org/10.1038/s41598-025-87284-1. Acesso em: 20 jun. 2025.
NETO, R. V. da S. et al. O papel da inteligência artificial no diagnóstico precoce de doenças ginecológicas. LUMEN E VIRTUS, v. 45, p. 712–723, 2025. DOI: 10.56238/levv16n45-001. Disponível em: https://periodicos.newsciencepubl.com/LEV/article/view/3099. Acesso em: 20 jun. 2025.
SILVA, C. D. F. B. et al. Tecnologia e inovação na obstetrícia: avanços que estão transformando a assistência ao parto. Revista Ibero-Americana de Humanidades, Ciências e Educação, v. 9, n. 8, p. 634–644, 2023. DOI: 10.51891/rease.v9i8.10914. Disponível em: https://periodicorease.pro.br/rease/article/view/10914. Acesso em: 21 jun. 2025.
SILVA, S. N. et al. Implementação de tecnologias em saúde no Brasil: análise de orientações federais para o sistema público de saúde. Ciência & Saúde Coletiva, v. 29, n. 1, p. e00322023, 2024.
TEIXEIRA, W. L. et al. Instructional guide to subsidize the nursing consultation in low-risk prenatal care: construction and validation. Cogitare Enfermagem, v. 28, 2023. Disponível em: https://dx.doi.org/10.1590/ce.v28i0.92037. Acesso em: 20 jun. 2025.
VASCONCELOS, F. A. G. Novos campos de atuação do nutricionista no Brasil: a emergência das inovações tecnológicas digitais, incluindo o uso da inteligência artificial. Revista de Nutrição, v. 38, e240088, 2025. Disponível em: https://doi.org/10.1590/1678-9865202538e240088pt. Acesso em: 20 jun. 2025.
