<?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>2014-9832</journal-id>
<journal-title><![CDATA[FEM: Revista de la Fundación Educación Médica]]></journal-title>
<abbrev-journal-title><![CDATA[FEM (Ed. impresa)]]></abbrev-journal-title>
<issn>2014-9832</issn>
<publisher>
<publisher-name><![CDATA[Fundación Educación Médica y Viguera Editores, S.L.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2014-98322025000300073</article-id>
<article-id pub-id-type="doi">10.33588/fem.283.1173</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Inteligencia artificial como espejo del razonamiento médico: ecosistemas cognitivos para una educación clínica inteligente]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial intelligence as a mirror of medical reasoning: cognitive ecosystems for intelligent clinical education]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[HERNÁNDEZ-BORROTO]]></surname>
<given-names><![CDATA[Carlos E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[HERNÁNDEZ]]></surname>
<given-names><![CDATA[Orquidia REYES-DE]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[MEDRANO-PLANA]]></surname>
<given-names><![CDATA[Yuri]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[FRANCO-SOLÓRZANO]]></surname>
<given-names><![CDATA[Verónica A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Médica de Villa Clara Universidad Médica de Villa Clara ]]></institution>
<addr-line><![CDATA[Santa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Grupo Médico de Ecodiagnóstico Departamento de Diagnóstico Médico ]]></institution>
<addr-line><![CDATA[Caracas ]]></addr-line>
<country>Venezuela</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Laica Eloy Alfaro de Manabí Escuela de Medicina ]]></institution>
<addr-line><![CDATA[Manta ]]></addr-line>
<country>Ecuador</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>28</volume>
<numero>3</numero>
<fpage>73</fpage>
<lpage>79</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.isciii.es/scielo.php?script=sci_arttext&amp;pid=S2014-98322025000300073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.isciii.es/scielo.php?script=sci_abstract&amp;pid=S2014-98322025000300073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.isciii.es/scielo.php?script=sci_pdf&amp;pid=S2014-98322025000300073&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[El desarrollo del juicio clínico es clave en la formación de profesionales de la salud, aunque los sesgos cognitivos siguen causando errores frecuentes. Ante esto, la educación médica ha implementado estrategias de reflexión y supervisión, aunque varían según el contexto. La inteligencia artificial (IA) promete revolucionar la enseñanza del razonamiento clínico, proponiendo su uso como un &#8216;espejo cognitivo&#8217; que ayuda a identificar patrones sesgados en el pensamiento del estudiante y fomentar la metacognición. Proponemos integrar la IA en un ecosistema de aprendizaje inteligente adaptativo, que combine arquitectura educativa interdisciplinaria y ética. En este marco, el razonamiento clínico se conceptualiza como procesos duales: intuitivo y heurístico (sistema 1); y analítico y deliberativo (sistema 2). Autores como Flavell y Croskerry han destacado la importancia de la metacognición para identificar sesgos y mejorar la toma de decisiones clínicas. La IA se emplearía para reflejar el pensamiento del estudiante, permitiéndole explorar sus patrones y errores mediante simulaciones clínicas y tutorías. Herramientas como el motor de retroalimentación metacognitiva analizan decisiones y sugieren reflexiones metacognitivas, promoviendo a los estudiantes a reconsiderar sus decisiones sin sobrevalorar el análisis deliberado. El ecosistema de aprendizaje inteligente adaptativo también ofrece soporte emocional y conexiones globales, fortaleciendo competencias clínicas en un entorno de aprendizaje flexible y personalizado. Sin embargo, este enfoque requiere consideración ética sobre la privacidad de datos, transparencia algorítmica y formación docente. La conjunción de IA y ecosistema adaptativo representa un avance hacia una educación médica más crítica y globalmente conectada.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[The development of clinical judgment is crucial in the training of healthcare professionals, though cognitive biases continue to lead to frequent errors. In response, medical education has implemented strategies such as reflection and supervision, though these vary depending on the context. Artificial intelligence (AI) promises to revolutionize the teaching of clinical reasoning by proposing its use as a &#8216;cognitive mirror&#8217; to help identify biased patterns in students&#8217; thinking and foster metacognition. I propose integrating AI into an adaptive intelligent learning ecosystem that combines interdisciplinary and ethical educational architecture. In this framework, clinical reasoning is conceptualized as dual processes: intuitive and heuristic (system 1) and analytical and deliberative (system 2). Authors like Flavell and Croskerry have emphasized the importance of metacognition in identifying biases and improving clinical decision-making. AI would be used to reflect on the student&#8217;s thinking, allowing them to explore their patterns and mistakes through clinical simulations and tutorials. Tools like the metacognitive feedback engine analyze decisions and suggest metacognitive reflections, encouraging students to reconsider their decisions without overvaluing deliberate analysis. The adaptive intelligent learning ecosystem also provides emotional support and global connections, strengthening clinical competencies in a flexible and personalized learning environment. However, this approach requires ethical consideration regarding data privacy, algorithmic transparency, and educator training. The combination of AI and adaptive ecosystem represents a step forward toward more critical and globally connected medical education.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Educación médica]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[Juicio clínico]]></kwd>
<kwd lng="es"><![CDATA[Metacognición]]></kwd>
<kwd lng="es"><![CDATA[Razonamiento clínico]]></kwd>
<kwd lng="es"><![CDATA[Sesgos cognitivos]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Clinical judgment]]></kwd>
<kwd lng="en"><![CDATA[Clinical reasoning]]></kwd>
<kwd lng="en"><![CDATA[Cognitive biases]]></kwd>
<kwd lng="en"><![CDATA[Medical education]]></kwd>
<kwd lng="en"><![CDATA[Metacognition]]></kwd>
</kwd-group>
</article-meta>
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