<?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>1684-1859</journal-id>
<journal-title><![CDATA[Revista Cubana de Informática Médica]]></journal-title>
<abbrev-journal-title><![CDATA[RCIM]]></abbrev-journal-title>
<issn>1684-1859</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de La Habana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1684-18592023000200013</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[El renacimiento tecnológico de la Radiología: la revolución open source y la inteligencia artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[The Technological Renaissance of Radiology: The Open Source Revolution and Artificial Intelligence]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tenajas]]></surname>
<given-names><![CDATA[Rebeca]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Miraut]]></surname>
<given-names><![CDATA[David]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centro de Salud de Arroyomolinos  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>España</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,GMV Innovating Solutions  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>España</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>15</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1684-18592023000200013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1684-18592023000200013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1684-18592023000200013&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El campo de la radiología ha experimentado avances notables en las últimas décadas, con desarrollos que van desde la mejora de la calidad y digitalización de las imágenes hasta la detección asistida por computadora. Particularmente, la aparición de técnicas de Inteligencia Artificial basadas en Deep Learning y Visión Computacional han promovido soluciones innovadoras en el diagnóstico y el análisis radiológico. Se explora la relevancia de los desarrollos y modelos open source en el progreso de estas técnicas, resaltando el impacto que la colaboración y el acceso abierto han tenido en el avance científico del campo. La investigación tiene un enfoque cualitativo, con alcance descriptivo y retrospectivo, de corte longitudinal. Se realizó un análisis documental de la evolución y el impacto del open source en la Radiología, poniendo de relieve la colaboración multidisciplinar. Se examinaron casos de uso, ventajas, desafíos y consideraciones éticas en relación con la implementación de soluciones basadas en Inteligencia Artificial en Radiología. El enfoque open source ha mostrado ser una influencia positiva en la Radiología, con potencial para influir en la atención médica, ofreciendo soluciones más precisas y accesibles. No obstante, se presentan desafíos éticos y técnicos que requieren atención.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The field of radiology has seen notable advances in recent decades, with developments ranging from image quality improvement and digitization to computer-aided detection. Particularly, the emergence of Artificial Intelligence techniques based on Deep Learning and Computer Vision have promoted innovative solutions in diagnosis and radiological analysis. This article explores the relevance of open source developments and models in the progress of these techniques, highlighting the impact that collaboration and open access have had on the scientific advancement in this field. This research has a qualitative approach, with a descriptive, retrospective, longitudinal scope. A documentary analysis of the evolution and impact of open source in Radiology was carried out, highlighting multidisciplinary collaboration. Use cases, advantages, challenges and ethical considerations were also examined in relation to the implementation of AI-based solutions in Radiology. The Open Source approach has been shown to be a positive influence in Radiology, with the potential to influence medical care, offering more precise and accessible solutions. However, there are ethical and technical challenges that require attention.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[radiología]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[código abierto]]></kwd>
<kwd lng="es"><![CDATA[desarrollo de software]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico por imagen]]></kwd>
<kwd lng="es"><![CDATA[ética médica]]></kwd>
<kwd lng="es"><![CDATA[difusión de innovación]]></kwd>
<kwd lng="en"><![CDATA[radiology]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[open source]]></kwd>
<kwd lng="en"><![CDATA[software development]]></kwd>
<kwd lng="en"><![CDATA[diagnostic imaging]]></kwd>
<kwd lng="en"><![CDATA[medical ethics]]></kwd>
<kwd lng="en"><![CDATA[diffusion of innovation]]></kwd>
</kwd-group>
</article-meta>
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