{"id":4812,"date":"2022-12-02T19:10:21","date_gmt":"2022-12-03T00:10:21","guid":{"rendered":"http:\/\/skimai.com\/?p=4812"},"modified":"2024-04-29T17:27:40","modified_gmt":"2024-04-29T22:27:40","slug":"blog-como-la-sanidad-podria-utilizar-la-ai-explicable","status":"publish","type":"post","link":"https:\/\/skimai.com\/es\/blog-how-healthcare-could-use-explainable-ai\/","title":{"rendered":"\u00bfC\u00f3mo podr\u00eda utilizar la sanidad la IA explicable?"},"content":{"rendered":"<p>\u00bfC\u00f3mo podr\u00eda utilizar la sanidad la IA explicable?<\/p>\n<p>La sanidad, las finanzas, los seguros y la fabricaci\u00f3n son s\u00f3lo algunos de los sectores en los que la inteligencia artificial (IA) tiene un gran impacto. Se est\u00e1n desarrollando modelos de IA m\u00e1s complejos para satisfacer los requisitos de casos de uso concretos. Sin embargo, las predicciones de estos modelos de IA parecen ser resultados de \"caja negra\", que carecen de cualquier justificaci\u00f3n o explicaci\u00f3n de por qu\u00e9 se hicieron. La necesidad de que investigadores, organizaciones y reguladores comprendan c\u00f3mo los modelos de IA dan completamente sugerencias, previsiones, etc., dio lugar a la IA explicable (XAI).<\/p>\n<p>La inteligencia artificial explicable (XAI) es una de las ramas m\u00e1s nuevas y de m\u00e1s r\u00e1pido crecimiento de la inteligencia artificial. El objetivo de la XAI es ofrecer una explicaci\u00f3n comprensible para el ser humano del modelo de aprendizaje profundo. En sectores en los que la seguridad es cr\u00edtica, como la sanidad o la seguridad, esto es extremadamente importante. Los enfoques presentados en la literatura a lo largo de los a\u00f1os afirman con frecuencia que responder\u00e1n a la pregunta de c\u00f3mo el modelo lleg\u00f3 a su conclusi\u00f3n directamente.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u00cdndice<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Alternar tabla de contenidos\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/skimai.com\/es\/blog-how-healthcare-could-use-explainable-ai\/#Understanding_When_XAI_is_Necessary_For_Healthcare\" >Comprender cu\u00e1ndo es necesaria la XAI para la atenci\u00f3n sanitaria<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/skimai.com\/es\/blog-how-healthcare-could-use-explainable-ai\/#Any_of_The_Following_Scenarios_Require_XAI\" >Cualquiera de los siguientes escenarios requiere XAI<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/skimai.com\/es\/blog-how-healthcare-could-use-explainable-ai\/#How_Can_Doctors_Benefit_From_XAI\" >\u00bfC\u00f3mo pueden beneficiarse los m\u00e9dicos de la XAI?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/skimai.com\/es\/blog-how-healthcare-could-use-explainable-ai\/#XAI_Can_Be_Used_By_Doctors_And_Healthcare_Professionals_As_They_Can\" >La XAI puede ser utilizada por m\u00e9dicos y profesionales sanitarios, ya que pueden<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/skimai.com\/es\/blog-how-healthcare-could-use-explainable-ai\/#Conclusion\" >Conclusi\u00f3n<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Understanding_When_XAI_is_Necessary_For_Healthcare\"><\/span>Comprender cu\u00e1ndo es necesaria la XAI para la atenci\u00f3n sanitaria<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Los profesionales sanitarios utilizan la IA para agilizar y mejorar diversas funciones, como la gesti\u00f3n de riesgos, la toma de decisiones e incluso el diagn\u00f3stico, escaneando im\u00e1genes m\u00e9dicas para encontrar anomal\u00edas y patrones invisibles para el ojo humano. Aunque la IA se ha convertido en una herramienta vital para muchos profesionales sanitarios, es dif\u00edcil de entender, lo que frustra a los proveedores, sobre todo a la hora de tomar decisiones importantes.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Any_of_The_Following_Scenarios_Require_XAI\"><\/span>Cualquiera de los siguientes escenarios requiere XAI<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>Cuando la equidad es una prioridad absoluta y los clientes o usuarios finales necesitan informaci\u00f3n para elegir con conocimiento de causa.<\/li>\n<li>Cuando un mal juicio de AI tiene efectos de amplio alcance (como una recomendaci\u00f3n de cirug\u00eda innecesaria).<\/li>\n<li>Cuando las consecuencias de un error son graves, como la clasificaci\u00f3n err\u00f3nea de tumores malignos, se producen gastos financieros excesivos, mayores riesgos para la salud y traumas personales.<\/li>\n<li>Cuando un sistema de IA crea una nueva hip\u00f3tesis que debe ser probada por especialistas en un campo concreto.<\/li>\n<li>Con el objetivo de cumplir la normativa, como el Reglamento General de Protecci\u00f3n de Datos (RGPD) de la UE, que garantiza el \"derecho a una explicaci\u00f3n\" cuando los procesos automatizados tratan datos de los usuarios<\/li>\n<\/ul>\n<p>Seg\u00fan varios expertos, la adopci\u00f3n relativamente tard\u00eda de los sistemas de IA en el sector sanitario se debe a la casi imposibilidad de confirmar de forma independiente los resultados de los sistemas de caja negra.<\/p>\n<p>Sin embargo, los m\u00e9dicos pueden utilizar la XAI para determinar el mejor tratamiento para un paciente determinando por qu\u00e9 tiene un riesgo tan alto de ingreso hospitalario. De este modo, los m\u00e9dicos pueden basar sus decisiones en informaci\u00f3n m\u00e1s fiable. Adem\u00e1s, mejora la trazabilidad y transparencia de las decisiones cl\u00ednicas. El proceso de aprobaci\u00f3n de productos farmac\u00e9uticos tambi\u00e9n puede acelerarse con XAI.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_Can_Doctors_Benefit_From_XAI\"><\/span>\u00bfC\u00f3mo pueden beneficiarse los m\u00e9dicos de la XAI?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2022\/12\/221208-draft1-1200x628-1.jpg\" alt=\"\" width=\"1200\" height=\"627\"><\/p>\n<p>Los monitores card\u00edacos de los relojes de pulsera de muchas personas pueden derretirse ante la idea de implantar la IA en la atenci\u00f3n sanitaria, pero creemos que un enfoque totalmente explicable y moral ser\u00e1 el chupete. Implicar\u00e1 que se pueda utilizar una gran cantidad de datos hist\u00f3ricos de pacientes y cl\u00ednicos para ayudar a los m\u00e9dicos y otros profesionales sanitarios a aprender de la IA, adem\u00e1s de ayudar a la IA a informar sobre los planes de atenci\u00f3n.<\/p>\n<p>Har\u00e1 posible un proceso eficaz, basado en datos, que eval\u00fae y calibre todo el algoritmo cada vez que se disponga de una nueva opci\u00f3n de tratamiento y permita trazar programas individualizados para cada paciente. Para mejorar continuamente sus recomendaciones a los m\u00e9dicos, combinar\u00e1 datos de miles de individuos con enfermedades casi comparables.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"XAI_Can_Be_Used_By_Doctors_And_Healthcare_Professionals_As_They_Can\"><\/span>La XAI puede ser utilizada por m\u00e9dicos y profesionales sanitarios, ya que pueden<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><\/p>\n<ul>\n<li>Aprender de los datos en lugar de dar autoridad al algoritmo. <\/li>\n<li>Tratar a los pacientes de forma imparcial y \u00e9tica utilizando una gran cantidad de informaci\u00f3n acad\u00e9mica y m\u00e9dica.<\/li>\n<li>Cree ese conocimiento con IA sin sacrificar el toque humano.<\/li>\n<li>Detectar los problemas a tiempo y tomar medidas m\u00e1s eficaces de intervenci\u00f3n para una recuperaci\u00f3n m\u00e1s r\u00e1pida de la salud.<\/li>\n<li>Mejore las instalaciones, la puntualidad, los presupuestos y los resultados sanitarios manteniendo bajos los costes.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusi\u00f3n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Cuando se trata de transformaci\u00f3n digital sostenible, muchas organizaciones ya aprovechan el crecimiento exponencial de la tecnolog\u00eda. Nunca ha habido un mejor momento para que los proveedores sanitarios y las empresas de los sectores de las ciencias de la vida y la biotecnolog\u00eda hagan lo mismo, ya que todos buscamos mejores formas de operar tras unos a\u00f1os turbulentos.<\/p>\n<p>Las soluciones de explicabilidad pueden ayudar a los profesionales sanitarios a mantener su confianza mientras exploran la innovaci\u00f3n. Adem\u00e1s, cuando los gobiernos impongan normativas rigurosas a la tecnolog\u00eda sanitaria, la explicabilidad puede ser un primer paso crucial para abrir la caja negra de la IA y aclarar a todas las partes interesadas la toma de decisiones sobre modelos.<\/p>","protected":false},"excerpt":{"rendered":"<p>How Healthcare Could Use Explainable AI? Healthcare, finance, insurance, and manufacturing are just a few industries where artificial intelligence (AI) greatly impacts. More complex AI models are being developed to meet the requirements of particular use cases. However, these AI models&#8217; predictions appear to be &quot;Black-box&quot; outputs, lacking any justification or explanation for why they [&hellip;]<\/p>\n","protected":false},"author":1003,"featured_media":4826,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[125,64],"tags":[],"class_list":["post-4812","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise-ai-blog","category-how-to"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How Healthcare Could Use Explainable AI? - Skim AI<\/title>\n<meta name=\"description\" content=\"The Healthcare Industry is rapidly evolving with new technologies. How Healthcare Could Use Explainable AI? 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