<?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>0034-7507</journal-id>
<journal-title><![CDATA[Revista Cubana de Estomatología]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Cubana Estomatol]]></abbrev-journal-title>
<issn>0034-7507</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ciencias Médicas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0034-75072024000100008</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Applications of Artificial Intelligence in Dentomaxillofacial Diagnosis]]></article-title>
<article-title xml:lang="es"><![CDATA[Aplicaciones de la inteligencia artificial en el diagnóstico dentomaxilofacial]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Suazo Galdames]]></surname>
<given-names><![CDATA[Iván]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autonoma de Chile Facultad de Ciencias de la Salud ]]></institution>
<addr-line><![CDATA[Santiago ]]></addr-line>
<country>Chile</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>61</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S0034-75072024000100008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S0034-75072024000100008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S0034-75072024000100008&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[SUMMARY  Introduction: The introduction of applications driven by artificial intelligence is revolutionizing dentomaxillofacial imaging.  Objectives: Describe the current state of the applications of artificial intelligence in dentomaxillofacial diagnosis, evaluate its impact, and identify future directions for research and implementation.  Method : A narrative review was carried out using systematic searches in databases such as PubMed, Google Scholar, IEEE Xplore, among others. The study focused on articles published from 2010 to the present. Research that applies artificial intelligence technologies in dentomaxillofacial diagnosis was included and its quality and relevance were evaluated using established tools.  Results: Artificial intelligence, especially deep learning, has shown significant improvements in image segmentation, disease detection, and treatment planning in dentomaxillofacial imaging. Artificial intelligence techniques have allowed the automation of image analysis tasks, improving efficiency and diagnostic accuracy.  Conclusions: Artificial intelligence has significant potential to revolutionize dentomaxillofacial imaging, as it offers improvements in diagnostic accuracy, efficiency in image interpretation, and treatment planning. Further research is needed to overcome technical, ethical, and privacy challenges and validate the clinical applicability of these technologies.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción: La introducción de aplicaciones impulsadas por la inteligencia artificial está revolucionando la imagenología dentomaxilofacial.  Objetivos: Describir el estado actual de las aplicaciones de la inteligencia artificial en el diagnóstico dentomaxilofacial; evaluar su impacto e identificar direcciones futuras para la investigación y la implementación.  Método : Se realizó una revisión narrativa, utilizando búsquedas sistemáticas en bases de datos como PubMed, Google Scholar, IEEE Xplore, entre otras; el estudio se enfocó en artículos publicados desde 2010 hasta la actualidad. Se incluyeron investigaciones que aplican tecnologías de la inteligencia artificial en el diagnóstico dentomaxilofacial; se evaluó su calidad y relevancia mediante las herramientas establecidas.  Resultados:  La inteligencia artificial, especialmente el aprendizaje profundo, ha mostrado mejoras significativas en la segmentación de imágenes, la detección de enfermedades y la planificación del tratamiento en imagenología dentomaxilofacial. Las técnicas de inteligencia artificial han permitido la automatización de tareas de análisis de imágenes, mejorado la eficiencia y la precisión diagnóstica.  Conclusiones: La inteligencia artificial posee un potencial significativo para revolucionar la imagenología dentomaxilofacial, pues ofrece mejoras en la precisión diagnóstica, eficiencia en la interpretación de imágenes y en la planificación del tratamiento. Se necesitan más investigaciones para superar desafíos técnicos, éticos y de privacidad y validar la aplicabilidad clínica de estas tecnologías.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[diagnostic imaging]]></kwd>
<kwd lng="en"><![CDATA[radiology]]></kwd>
<kwd lng="en"><![CDATA[X-ray computed tomography machines]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico por imagen]]></kwd>
<kwd lng="es"><![CDATA[radiología]]></kwd>
<kwd lng="es"><![CDATA[tomógrafos computarizados por rayos X]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
</kwd-group>
</article-meta>
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<article-title xml:lang=""><![CDATA[Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics]]></article-title>
<source><![CDATA[Eur J Nucl Med Mol Imaging]]></source>
<year>2019</year>
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