<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1646-5830</journal-id>
<journal-title><![CDATA[Acta Obstétrica e Ginecológica Portuguesa]]></journal-title>
<abbrev-journal-title><![CDATA[Acta Obstet Ginecol Port]]></abbrev-journal-title>
<issn>1646-5830</issn>
<publisher>
<publisher-name><![CDATA[Euromédice, Edições Médicas Lda.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1646-58302025000300164</article-id>
<article-id pub-id-type="doi">10.69729/aogp.v19i3a02</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Artificial intelligence on prenatal ultrasound: advantages and limitations]]></article-title>
<article-title xml:lang="pt"><![CDATA[Inteligência artificial na ecografia pré-natal: vantagens e limitações]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz]]></surname>
<given-names><![CDATA[Jader]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guedes-Martins]]></surname>
<given-names><![CDATA[Luís]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Associação Portuguesa de Diagnóstico Pré-Natal  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Portugal</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<volume>19</volume>
<numero>3</numero>
<fpage>164</fpage>
<lpage>168</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-58302025000300164&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-58302025000300164&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-58302025000300164&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The integration of artificial intelligence (AI) into prenatal ultrasound represents one of the most promising innovations in contemporary Fetal Medicine. This opinion article examines the main advantages and limitations of AI application in this context, highlighting advances in the automation of biometric measurements, reduction of clinicians&#8217; cognitive workload, and diagnostic support for fetal anomalies - particularly cardiac and central nervous system malformations. The use of convolutional neural networks has shown high efficacy in the segmentation and detection of fetal structures, enhancing both efficiency and consistency in screening. However, several challenges remain, including the need for large and diverse datasets, technical constraints, ethical considerations, and difficulties in effective implementation within clinical practice. The widespread adoption of these technologies will depend on continued research, appropriate regulatory frameworks, and close collaboration between clinicians and engineers, ensuring safe, effective, and equitable integration across varied healthcare settings.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A integração da inteligência artificial (IA) na ecografia pré-natal constitui uma das mais promissoras inovações na Medicina Fetal contemporânea. Este artigo de opinião analisa as principais vantagens e limitações da aplicação da IA neste contexto, salientando os avanços na automatização das medições biométricas, a redução da carga cognitiva dos profissionais de saúde e o apoio ao diagnóstico de anomalias fetais, com especial destaque para as malformações cardíacas e do sistema nervoso central. A utilização de redes neuronais convolucionais tem demonstrado elevada eficácia na segmentação e deteção de estruturas fetais, promovendo maior eficiência e consistência no rastreio. Contudo, subsistem desafios relevantes, nomeadamente a necessidade de bases de dados amplas e diversificadas, constrangimentos técnicos, questões éticas e dificuldades na integração efetiva na prática clínica. A plena adoção destas tecnologias dependerá de investigação contínua, regulamentação adequada e da colaboração entre clínicos e engenheiros, de forma a assegurar uma implementação segura, eficaz e equitativa em diferentes realidades assistenciais.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Prenatal ultrasound]]></kwd>
<kwd lng="en"><![CDATA[Fetal medicine]]></kwd>
<kwd lng="en"><![CDATA[Automated diagnosis]]></kwd>
<kwd lng="pt"><![CDATA[Inteligência artificial]]></kwd>
<kwd lng="pt"><![CDATA[Ecografia pré-natal]]></kwd>
<kwd lng="pt"><![CDATA[Medicina fetal]]></kwd>
<kwd lng="pt"><![CDATA[Diagnóstico automatizado]]></kwd>
</kwd-group>
</article-meta>
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