<?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>2007-5057</journal-id>
<journal-title><![CDATA[Investigación en educación médica]]></journal-title>
<abbrev-journal-title><![CDATA[Investigación educ. médica]]></abbrev-journal-title>
<issn>2007-5057</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Medicina]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-50572025000400146</article-id>
<article-id pub-id-type="doi">10.22201/fm.20075057e.2025.56.25735</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Inteligencia artificial en la educación médica continua: ¿aliada inevitable o simplemente una herramienta más?]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial Intelligence in Continuing Medical Education: Inevitable ally or just another tool?]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Margolis]]></surname>
<given-names><![CDATA[Alvaro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Red Latinoamericana de Educación Médica Continua  ]]></institution>
<addr-line><![CDATA[Montevideo ]]></addr-line>
<country>Uruguay</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>14</volume>
<numero>56</numero>
<fpage>146</fpage>
<lpage>153</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-50572025000400146&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-50572025000400146&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-50572025000400146&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La irrupción de la inteligencia artificial generativa (IA) ha introducido transformaciones significativas en la educación médica continua (EMC) y, en términos más amplios, en el desarrollo profesional continuo (DPC). Este ensayo busca analizar críticamente su incorporación, no desde una perspectiva tecnológica, sino enfocándose en el valor que puede aportar a instituciones, docentes y profesionales de la salud. La IA puede generar valor al aumentar la eficiencia (por ejemplo, traducción automática o redacción de resúmenes), mejorar la calidad (como la creación asistida de materiales educativos) y posibilitar prácticas previamente inviables (como el acompañamiento tutorial personalizado en tiempo real). Se exponen casos de uso en contextos formales (cursos, congresos) e informales (aprendizaje en el lugar de trabajo), destacando que la mayoría del aprendizaje clínico ocurre en este último entorno. También se revisan las competencias necesarias para una adopción responsable por parte de educadores, y se señalan riesgos relevantes como la privacidad, la posibilidad de errores, los sesgos culturales y los dilemas éticos en torno a la transparencia y la autoría. Aun así, se propone evaluar la utilidad de estos sistemas comparándolos con el &#8220;mejor humano disponible&#8221; para consultar, que va a ser distinto en cada contexto. Finalmente, se evidencia una rápida adopción de la IA por parte de los médicos, en contraste con una incorporación institucional más lenta. Para promover su integración organizacional se plantean estrategias que incluyen políticas de experimentación, liderazgo activo, trabajo interdisciplinario y la inclusión de la IA en los flujos operativos. En conclusión, la IA no es solo una herramienta adicional, sino una aliada inevitable en la EMC, cuyo impacto dependerá de su alineación con necesidades reales y su integración ética y contextualizada.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The emergence of generative artificial intelligence (AI) has brought about significant transformations in continuing medical education (CME) and, more broadly, in continuing professional development (CPD). This essay aims to critically analyze its incorporation, not from a technological standpoint, but by focusing on the value it can bring to institutions, educators, and healthcare professionals. AI can add value by increasing efficiency (e.g., automatic translation or summarization), improving quality (such as assisted creation of educational materials), and enabling previously unfeasible practices (like real-time personalized tutoring support). Use cases are presented in both formal settings (courses, conferences) and informal ones (workplace learning), highlighting that most clinical learning occurs in the latter. The necessary competencies for responsible adoption by educators are also reviewed, along with key risks such as privacy concerns, the possibility of errors, cultural biases, and ethical dilemmas related to transparency and authorship. Nevertheless, it is suggested that the usefulness of these systems should be evaluated in comparison to &#8220;the best available human&#8221; for consultation-which will vary depending on the context. Finally, a rapid adoption of AI by physicians is noted, in contrast with a slower institutional integration. To support its organizational uptake, proposed strategies include experimentation policies, active leadership, interdisciplinary collaboration, and the inclusion of AI in operational workflows. In conclusion, AI is not merely an additional tool, but an inevitable ally in CME, with its impact depending on alignment with real needs and its ethical, context-sensitive integration.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Educación médica continua]]></kwd>
<kwd lng="es"><![CDATA[desarrollo profesional continuo]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial generativa]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje informal]]></kwd>
<kwd lng="es"><![CDATA[adopción de la IA]]></kwd>
<kwd lng="en"><![CDATA[Continuing medical education]]></kwd>
<kwd lng="en"><![CDATA[continuing professional development]]></kwd>
<kwd lng="en"><![CDATA[generative artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[informal learning]]></kwd>
<kwd lng="en"><![CDATA[AI adoption]]></kwd>
</kwd-group>
</article-meta>
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