<?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>1727-897X</journal-id>
<journal-title><![CDATA[MediSur]]></journal-title>
<abbrev-journal-title><![CDATA[Medisur]]></abbrev-journal-title>
<issn>1727-897X</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de Cienfuegos, Centro Provincial de Ciencias Médicas, Provincia de Cienfuegos.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1727-897X2022000200341</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Inteligencia artificial explicable, una perspectiva al problema de la clasificación automática de COVID-19 mediante radiografías de tórax]]></article-title>
<article-title xml:lang="en"><![CDATA[Explainable artificial intelligence, a perspective to the automatic classification of COVID-19 through chest X-rays problem]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Cabrera]]></surname>
<given-names><![CDATA[José Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Díaz]]></surname>
<given-names><![CDATA[Marlen]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Facultad de Matemática, Física y Computación. Universidad Central Marta Abreu de Las Villas  ]]></institution>
<addr-line><![CDATA[Villa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Facultad de Ingeniería Eléctrica. Universidad Central Marta Abreu de Las Villas  ]]></institution>
<addr-line><![CDATA[Villa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<volume>20</volume>
<numero>2</numero>
<fpage>341</fpage>
<lpage>351</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1727-897X2022000200341&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1727-897X2022000200341&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1727-897X2022000200341&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Esta investigación pretende dilucidar, a partir del análisis de técnicas de inteligencia artificial explicables, la robustez y el nivel de generalización de los métodos de visión por computadora propuestos para identificar COVID-19 utilizando imágenes de radiografías de tórax. Asimismo, alertar a los investigadores y revisores sobre el problema del aprendizaje por atajos. En este estudio se siguen recomendaciones para identificar si los modelos de clasificación automática de COVID-19 se ven afectados por el aprendizaje por atajos. Para ello, se revisaron los artículos que utilizan métodos de inteligencia artificial explicable en dicha tarea. Se evidenció que al utilizar la imagen de radiografía de tórax completa o el cuadro delimitador de los pulmones, las regiones de la imagen que más contribuyen a la clasificación aparecen fuera de la región pulmonar, algo que no tiene sentido. Los resultados indican que, hasta ahora, los modelos existentes presentan el problema de aprendizaje por atajos, lo cual los hace inapropiados para ser usados en entornos clínicos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This research aims to elucidate, from the analysis of explainable artificial intelligence techniques, the robustness and level of generalization of the proposed computer vision methods to identify COVID-19 using chest X-ray images. Also, alert researchers and reviewers about the problem of learning by shortcuts. In this study, recommendations are followed to identify if the automatic classification models of COVID-19 are affected by shortcut learning. To do this, articles that use explainable artificial intelligence methods were reviewed. It was shown that when using the full chest X-ray image or the bounding box of the lungs, the regions of the image that contribute the most to the classification appear outside the lung region, something that does not make sense. The results indicate that, so far, the existing models present the problem of learning by shortcuts, which makes them inappropriate to be used in clinical settings.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="es"><![CDATA[radiografía torácica]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[radiography, thoracic]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
</kwd-group>
</article-meta>
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