{"id":43,"date":"2025-07-22T11:10:00","date_gmt":"2025-07-22T11:10:00","guid":{"rendered":"https:\/\/healthscienceinstitute.org\/?p=43"},"modified":"2025-12-09T09:38:29","modified_gmt":"2025-12-09T09:38:29","slug":"the-role-of-ai-in-healthcare-enhancing-diagnostic-and-therapy-precision","status":"publish","type":"post","link":"https:\/\/scienceinhealth.com\/es\/2025\/07\/22\/the-role-of-ai-in-healthcare-enhancing-diagnostic-and-therapy-precision\/","title":{"rendered":"El papel de la IA en la atenci\u00f3n sanitaria: mejorar la precisi\u00f3n diagn\u00f3stica y terap\u00e9utica"},"content":{"rendered":"<h3 class=\"wp-block-heading\">Puntos clave<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>La inteligencia artificial (IA) est\u00e1 redefiniendo el diagn\u00f3stico y la terapia personalizada a trav\u00e9s de herramientas como el aprendizaje autom\u00e1tico, la visi\u00f3n por computadora y el procesamiento del lenguaje natural.<\/li>\n\n\n\n<li>Los sistemas impulsados por la IA han mejorado la precisi\u00f3n del diagn\u00f3stico en campos como radiolog\u00eda, dermatolog\u00eda, cardiolog\u00eda y oncolog\u00eda.<\/li>\n\n\n\n<li>La terapia personalizada se est\u00e1 volviendo m\u00e1s adaptativa y precisa, gracias al descubrimiento de f\u00e1rmacos basado en la IA, la gen\u00f3mica y la tecnolog\u00eda port\u00e1til.<\/li>\n\n\n\n<li>La integraci\u00f3n de la IA en el cuidado de la salud plantea preocupaciones \u00e9ticas, reglamentarias y log\u00edsticas, incluidos los prejuicios, la privacidad de los datos y la confianza de los m\u00e9dicos.<\/li>\n\n\n\n<li>La implementaci\u00f3n en el mundo real muestra tanto promesa como resistencia; el futuro de la IA en medicina depende de la colaboraci\u00f3n entre humanos y IA y una supervisi\u00f3n reglamentaria clara.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Una revoluci\u00f3n que no se puede ignorar<\/h2>\n\n\n\n<p>En 2016, DeepMind de Google sorprendi\u00f3 al mundo m\u00e9dico cuando su sistema de IA super\u00f3 a los radi\u00f3logos expertos en la detecci\u00f3n de m\u00e1s de 50 condiciones oculares utilizando esc\u00e1neres de retina, logrando una precisi\u00f3n del 94% en la clasificaci\u00f3n de casos urgentes-a menudo atrapando lo que los m\u00e9dicos humanos no han visto (<a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41591-018-0107-6\">De Fauw y otros, 2018<\/a>). Avance r\u00e1pido a hoy, y AI no es solo el an\u00e1lisis de im\u00e1genes. Se trata de predecir los problemas card\u00edacos, detectar el c\u00e1ncer antes y guiar la terapia basada en la biolog\u00eda individual.<\/p>\n\n\n\n<p>La inteligencia artificial en el cuidado de la salud no es ciencia ficci\u00f3n; ahora es un socio para el diagn\u00f3stico y la toma de decisiones. Pero con su promesa viene preguntas cr\u00edticas: \u00bfPodemos confiar en los algoritmos con llamadas de vida o muerte? \u00bfQui\u00e9n asegura que estas herramientas son justas y seguras? Este art\u00edculo explora c\u00f3mo la IA est\u00e1 transformando el cuidado de la salud y qu\u00e9 est\u00e1 en juego si nos equivocamos.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00bfQu\u00e9 es la IA en el cuidado de la salud, realmente?<\/h2>\n\n\n\n<p>En su esencia, la IA en el cuidado de la salud se refiere a los sistemas inform\u00e1ticos que simulan la inteligencia humana para extraer informaci\u00f3n de datos m\u00e9dicos. Estos sistemas se dividen en categor\u00edas como:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Aprendizaje autom\u00e1tico (ML):<\/strong> Se utiliza para predecir el riesgo o los resultados de las enfermedades mediante la formaci\u00f3n a partir de grandes conjuntos de datos (<a>Jiang y otros, 2017<\/a>).<\/li>\n\n\n\n<li><strong>Aprendizaje Profundo:<\/strong> Especialmente \u00fatil para la interpretaci\u00f3n de im\u00e1genes, como en radiolog\u00eda y dermatolog\u00eda.<\/li>\n\n\n\n<li><strong>Procesamiento del lenguaje natural (NLP):<\/strong> Se utiliza para extraer informaci\u00f3n de registros m\u00e9dicos electr\u00f3nicos (EMR), documentos de investigaci\u00f3n y notas cl\u00ednicas (<a>Shickel y otros, 2018<\/a>).<\/li>\n\n\n\n<li><strong>Visi\u00f3n por Computador:<\/strong> Permite que la IA interprete im\u00e1genes diagn\u00f3sticas como radiograf\u00edas o diapositivas de patolog\u00eda.<\/li>\n<\/ul>\n\n\n\n<p>Estas tecnolog\u00edas han evolucionado desde los sistemas basados en reglas de la d\u00e9cada de 1980 hasta los modelos actuales impulsados por datos, gracias a una capacidad inform\u00e1tica m\u00e1s accesible y a los datos m\u00e9dicos digitalizados.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Diagnosticar con m\u00e1s precisi\u00f3n que nunca<\/h2>\n\n\n\n<p>La IA ya est\u00e1 superando a los seres humanos en determinadas tareas de diagn\u00f3stico. Un estudio <em>naturaleza<\/em> revel\u00f3 que el modelo de aprendizaje profundo de Google Health redujo los falsos positivos en un 5,7% y los falsos negativos en un 9,4% en las pruebas de detecci\u00f3n del c\u00e1ncer de mama en comparaci\u00f3n con seis radi\u00f3logos expertos (<a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-019-1799-6\">McKinney y otros, 2020<\/a>).<\/p>\n\n\n\n<p>En situaciones de emergencia, plataformas como Aidoc ayudan a los profesionales de la salud a informar sobre embolismos pulmonares o apoplej\u00edas en cuesti\u00f3n de minutos, lo que reduce el tiempo de tratamiento y mejora las tasas de supervivencia (<a>Aidoc, 2023<\/a>). Mientras tanto, Tempus combina patolog\u00eda, EMR y gen\u00f3mica para informar las decisiones de terapia contra el c\u00e1ncer, especialmente en casos complejos.<\/p>\n\n\n\n<p>La dermatolog\u00eda tambi\u00e9n est\u00e1 experimentando una transformaci\u00f3n. Los modelos de IA entrenados en miles de im\u00e1genes de lesiones cut\u00e1neas pueden detectar el melanoma con una precisi\u00f3n comparable o superior a la de los dermat\u00f3logos, seg\u00fan un <em>Lancet Digital Health<\/em> estudiar (<a>Tschandl y otros, 2020<\/a>).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">De un tama\u00f1o \u00fanico a una terapia personalizada<\/h2>\n\n\n\n<p>La IA est\u00e1 revolucionando la transici\u00f3n de la atenci\u00f3n reactiva a la proactiva:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>f\u00e1rmacos:<\/strong> Las empresas como Insilico Medicine utilizan la IA para modelar c\u00f3mo los medicamentos interact\u00faan con la biolog\u00eda humana, acelerando el descubrimiento y reduciendo los costes de I+D (<a>Zhou y otros, 2021<\/a>).<\/li>\n\n\n\n<li><strong>Gen\u00f3mica:<\/strong> AlphaFold de DeepMind resolvi\u00f3 el desaf\u00edo de d\u00e9cadas de predecir el plegamiento de prote\u00ednas con precisi\u00f3n casi de laboratorio, desbloqueando conocimientos sobre problemas intestinales inflamatorios, condiciones autoinmunes y biolog\u00eda del c\u00e1ncer (<a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-020-2649-2\">Senior y otros, 2020<\/a>).<\/li>\n\n\n\n<li><strong>Dispositivos port\u00e1tiles:<\/strong> Las tecnolog\u00edas inteligentes ahora rastrean la biometr\u00eda y activan intervenciones en tiempo real. Los sistemas de neuromodulaci\u00f3n vagal impulsados por la IA, por ejemplo, se est\u00e1n explorando para apoyar din\u00e1micamente el equilibrio mental, reducir los s\u00edntomas del trauma y ayudar a los problemas del sue\u00f1o (<a>Fauver et al., 2021<\/a>).<\/li>\n<\/ul>\n\n\n\n<p>La medicina personalizada guiada por IA no solo aumenta la efectividad sino que tambi\u00e9n reduce los efectos secundarios al tener en cuenta la gen\u00e9tica espec\u00edfica del usuario, las comorbilidades y el comportamiento.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Obst\u00e1culos \u00e9ticos, cl\u00ednicos y reglamentarios<\/h2>\n\n\n\n<p>Sin embargo, la revoluci\u00f3n de la IA trae desaf\u00edos reales.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Sesgo en los conjuntos de datos:<\/strong> Se encontr\u00f3 que una herramienta de IA ampliamente utilizada en hospitales de EE. UU., sistem\u00e1ticamente subestima las necesidades de atenci\u00f3n de los usuarios negros al basarse en el gasto hist\u00f3rico en salud en lugar de datos cl\u00ednicos (<a>Obermeyer et al., 2019<\/a>).<\/li>\n\n\n\n<li><strong>Explicabilidad:<\/strong> La mayor\u00eda de los modelos de IA, especialmente las redes neuronales profundas, se consideran \"cajas negras\", por lo que es dif\u00edcil explicar sus decisiones-una barrera a la confianza cl\u00ednica (<a class=\"\" href=\"https:\/\/arxiv.org\/abs\/1702.08608\">Doshi-Velez &amp; Kim, 2017<\/a>).<\/li>\n\n\n\n<li><strong>Aislamiento:<\/strong> Garantizar el cumplimiento de HIPAA y GDPR es especialmente complejo cuando las herramientas de inteligencia artificial acceden a flujos de datos en tiempo real.<\/li>\n\n\n\n<li><strong>Supervisi\u00f3n reglamentaria:<\/strong> La FDA ha aprobado varias herramientas basadas en IA, incluyendo IDx-DR para el cribado ocular diab\u00e9tico, bajo su marco de software como dispositivo m\u00e9dico (SaMD) (<a>FDA, 2020<\/a>).<\/li>\n<\/ul>\n\n\n\n<p>Sin embargo, los criterios de aprobaci\u00f3n var\u00edan a nivel mundial. Si bien los EE.UU. y la UE est\u00e1n desarrollando v\u00edas de adaptaci\u00f3n para las herramientas de IA, la mayor\u00eda de los pa\u00edses todav\u00eda carecen de planes regulatorios s\u00f3lidos.<\/p>\n\n\n\n<p><strong><strong><a href=\"http:\/\/scienceinhealth.com\/es\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">Suscr\u00edbase gratis para obtener art\u00edculos de salud m\u00e1s perspicaces adaptados a sus necesidades.<\/a><\/strong><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00bfHay profesionales de la salud a bordo?<\/h2>\n\n\n\n<p>Depende. En un 2023 <em>Stanford Medicine<\/em> informe, el 85% de los profesionales de la salud dijo que la IA pod\u00eda ayudar a reducir las cargas de documentaci\u00f3n, pero s\u00f3lo el 38% confiaba en ella para tomar decisiones diagn\u00f3sticas (<a>Stanford Medicine 2023 Health Trends Report<\/a>).<\/p>\n\n\n\n<p>La resistencia a menudo proviene de las preocupaciones sobre la interrupci\u00f3n del flujo de trabajo, las necesidades de formaci\u00f3n y la responsabilidad. Sin embargo, los modelos colaborativos, en los que la IA apoya -pero no anula- el juicio cl\u00ednico, est\u00e1n ganando popularidad. Instituciones como Mayo Clinic y Cleveland Clinic ahora est\u00e1n dise\u00f1ando conjuntamente soluciones de IA con la participaci\u00f3n de los m\u00e9dicos para garantizar su relevancia y usabilidad.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">El futuro: lo que viene<\/h2>\n\n\n\n<p>lo que viene?<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>IA generativa<\/strong> Se est\u00e1n probando herramientas como ChatGPT y Med-PaLM para la documentaci\u00f3n, el apoyo a las decisiones y la educaci\u00f3n de los pacientes (<a class=\"\" href=\"https:\/\/arxiv.org\/abs\/2302.07081\">Singhal et al., 2022<\/a>).<\/li>\n\n\n\n<li><strong>aprendizaje federado<\/strong> permite que los modelos de IA sean entrenados a trav\u00e9s de fuentes de datos descentralizadas, minimizando los riesgos de privacidad y mejorando la precisi\u00f3n.<\/li>\n\n\n\n<li><strong>Inteligencia artificial en salud mental:<\/strong> Las herramientas que analizan la voz, el texto o las expresiones faciales pueden detectar pronto traumas, pensamientos ansiosos o estados depresivos antes de que los s\u00edntomas se hagan visibles (<a>Guntuku y otros, 2017<\/a>).<\/li>\n<\/ul>\n\n\n\n<p>Pero a pesar de todo lo que promete, la IA debe demostrar su valor no solo en estudios de laboratorio, sino tambi\u00e9n en la pr\u00e1ctica del mundo real, entre usuarios diversos, datos imperfectos y profesionales de la salud con poco tiempo.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00bfA d\u00f3nde vamos desde aqu\u00ed?<\/h2>\n\n\n\n<p>La inteligencia artificial tiene el potencial de mejorar casi todas las facetas de la medicina moderna, pero solo cuando est\u00e1 guiada por la transparencia, la equidad y la \u00e9tica. Los profesionales de la salud deben buscar herramientas que mejoren, no compliquen, su flujo de trabajo. Los formuladores de pol\u00edticas deben impulsar sistemas justos, explicables y seguros. Y los usuarios merecen visibilidad sobre c\u00f3mo los algoritmos influyen en su atenci\u00f3n.<\/p>\n\n\n\n<p>Para obtener m\u00e1s informaci\u00f3n sobre las herramientas m\u00e9dicas de IA aprobadas, visite <a>Listados de SaMD de la FDA<\/a> o explorar la <a class=\"\" href=\"https:\/\/www.who.int\/publications\/i\/item\/9789240029200\">Gu\u00eda 2021 de la Organizaci\u00f3n Mundial de la Salud sobre la \u00e9tica de la IA en salud<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Fuentes<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>De Fauw, J., et al. (2018). <em>Clinically applicable deep learning for diagnosis and referral in retinal disease<\/em>. Nature Medicine. <a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41591-018-0107-6\">https:\/\/www.nature.com\/articles\/s41591-018-0107-6<\/a><\/li>\n\n\n\n<li>McKinney, S. M., et al. (2020). <em>International evaluation of an AI system for breast cancer screening<\/em>. Nature. <a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-019-1799-6\">https:\/\/www.nature.com\/articles\/s41586-019-1799-6<\/a><\/li>\n\n\n\n<li>Aidoc (2023). <em>Case Studies and Clinical Impact.<\/em> <a>https:\/\/www.aidoc.com\/resources\/<\/a><\/li>\n\n\n\n<li>Tschandl, P., et al. (2020). <em>Human\u2013computer collaboration for skin cancer recognition<\/em>. The Lancet Digital Health. <a>https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(19)30200-8\/fulltext<\/a><\/li>\n\n\n\n<li>Zhou, Y., et al. (2021). <em>AI in drug discovery: Insights and challenges<\/em>. Drug Discovery Today. <a>https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8056493\/<\/a><\/li>\n\n\n\n<li>Senior, A. W., et al. (2020). <em>AlphaFold: Protein structure prediction<\/em>. Nature. <a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-020-2649-2\">https:\/\/www.nature.com\/articles\/s41586-020-2649-2<\/a><\/li>\n\n\n\n<li>Fauver, J. R., et al. (2021). <em>Neuroadaptive therapy platforms in mental health.<\/em> PubMed. <a>https:\/\/pubmed.ncbi.nlm.nih.gov\/34545342\/<\/a><\/li>\n\n\n\n<li>Obermeyer, Z., et al. (2019). <em>Dissecting racial bias in an algorithm used to manage population health<\/em>. Science. <a>https:\/\/www.science.org\/doi\/10.1126\/science.aax2342<\/a><\/li>\n\n\n\n<li>Doshi-Velez, F., &amp; Kim, B. (2017). <em>Towards a rigorous science of interpretable machine learning<\/em>. <a class=\"\" href=\"https:\/\/arxiv.org\/abs\/1702.08608\">https:\/\/arxiv.org\/abs\/1702.08608<\/a><\/li>\n\n\n\n<li>FDA (2020). <em>Software as a Medical Device (SaMD)<\/em>. <a>https:\/\/www.fda.gov\/medical-devices\/digital-health-center-excellence\/software-medical-device-samd<\/a><\/li>\n\n\n\n<li>Stanford Medicine (2023). <em>Health Trends Report<\/em>. <a>https:\/\/med.stanford.edu\/content\/dam\/sm\/sm-news\/documents\/StanfordMedicine_HealthTrendsReport2023.pdf<\/a><\/li>\n\n\n\n<li>Singhal, K., et al. (2022). <em>Med-PaLM: Large Language Models in Medicine<\/em>. <a class=\"\" href=\"https:\/\/arxiv.org\/abs\/2302.07081\">https:\/\/arxiv.org\/abs\/2302.07081<\/a><\/li>\n\n\n\n<li>Guntuku, S. C., et al. (2017). <em>Detecting mental health from social media<\/em>. Journal of Medical Internet Research. <a>https:\/\/www.jmir.org\/2017\/5\/e75\/<\/a><\/li>\n<\/ol>\n\n\n\n<p><strong>AI Diagnostics &amp; Clinical Performance<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>De Fauw, J., Ledsam, J. R., Romera-Paredes, B., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24, 1342\u20131350. <a href=\"https:\/\/doi.org\/10.1038\/s41591-018-0107-6\">https:\/\/doi.org\/10.1038\/s41591-018-0107-6<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>McKinney, S. M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577, 89\u201394. <a href=\"https:\/\/doi.org\/10.1038\/s41586-019-1799-6\">https:\/\/doi.org\/10.1038\/s41586-019-1799-6<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"3\" class=\"wp-block-list\">\n<li>Tschandl, P., Rinner, C., Apalla, Z., et al. (2020). Human\u2013computer collaboration for skin cancer recognition. The Lancet Digital Health, 2(9), e489\u2013e499. <a href=\"https:\/\/doi.org\/10.1016\/S2589-7500(20)30159-5\">https:\/\/doi.org\/10.1016\/S2589-7500(20)30159-5<\/a><\/li>\n<\/ol>\n\n\n\n<p><strong>Drug Discovery &amp; Genomics<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Zhou, Y., Li, Z., &amp; Xu, M. (2021). Artificial intelligence in drug discovery: Applications and challenges. Drug Discovery Today, 26(5), 1216\u20131227. <a href=\"https:\/\/doi.org\/10.1016\/j.drudis.2021.02.005\">https:\/\/doi.org\/10.1016\/j.drudis.2021.02.005<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Senior, A. W., Evans, R., Jumper, J., et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577, 706\u2013710. <a href=\"https:\/\/doi.org\/10.1038\/s41586-019-1923-7\">https:\/\/doi.org\/10.1038\/s41586-019-1923-7<\/a><\/li>\n<\/ol>\n\n\n\n<p><strong>Neuroadaptive \/ Neuromodulation Tech<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Fauver, J. R., Fletcher, R. R., &amp; Morris, M. E. (2021). Towards neuroadaptive therapy platforms for mental health. Frontiers in Neuroscience, 15, 739074. <a href=\"https:\/\/doi.org\/10.3389\/fnins.2021.739074\">https:\/\/doi.org\/10.3389\/fnins.2021.739074<\/a><\/li>\n<\/ol>\n\n\n\n<p><strong>AI Bias, Ethics &amp; Explainability<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Obermeyer, Z., Powers, B., Vogeli, C., &amp; Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage population health. Science, 366(6464), 447\u2013453. <a href=\"https:\/\/doi.org\/10.1126\/science.aax2342\">https:\/\/doi.org\/10.1126\/science.aax2342<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Doshi-Velez, F., &amp; Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.1702.08608<\/li>\n<\/ol>\n\n\n\n<p><strong>NLP &amp; Clinical Data Analytics<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Shickel, B., Tighe, P. J., Bihorac, A., &amp; Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589\u20131604. <a href=\"https:\/\/doi.org\/10.1109\/JBHI.2017.2767063\">https:\/\/doi.org\/10.1109\/JBHI.2017.2767063<\/a><\/li>\n<\/ol>\n\n\n\n<p><em>El art\u00edculo no constituye en modo alguno un consejo m\u00e9dico. Consulte con un profesional m\u00e9dico autorizado antes de iniciar cualquier tratamiento. Este sitio web puede recibir comisiones por los enlaces o productos mencionados en este art\u00edculo.<\/em><\/p>\n\n\n\n<p><strong><a href=\"http:\/\/scienceinhealth.com\/es\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">Suscr\u00edbase gratis para obtener art\u00edculos de salud m\u00e1s perspicaces adaptados a sus necesidades.<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"<p>Key Points A Revolution You Can\u2019t Ignore In 2016, Google\u2019s DeepMind shocked the medical world when its AI system&hellip;<\/p>","protected":false},"author":13,"featured_media":44,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[23,18],"tags":[310,147],"class_list":["post-43","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-in-healthcare","category-health-tech","tag-ai-diagnostics","tag-ai-in-healthcare"],"yoast_head":"<!-- This site is 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