{"id":10464,"date":"2020-10-20T09:23:52","date_gmt":"2020-10-20T14:23:52","guid":{"rendered":"https:\/\/saluddigital.com\/?p=10464"},"modified":"2025-10-21T14:21:24","modified_gmt":"2025-10-21T20:21:24","slug":"guia-sobre-aspectos-generales-y-complementarios-sobre-inteligencia-artificial-para-profesionales-de-la-salud-es-publicada-en-nature","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/comunidades-conectadas\/guia-sobre-aspectos-generales-y-complementarios-sobre-inteligencia-artificial-para-profesionales-de-la-salud-es-publicada-en-nature\/","title":{"rendered":"Guide to General and Complementary Aspects on Artificial Intelligence for Health Professionals was published on Nature"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"10464\" class=\"elementor elementor-10464\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-daca0d0 elementor-section-boxed elementor-section-height-default elementor-section-height-default wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no\" data-id=\"daca0d0\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-248660c\" data-id=\"248660c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bb69dd6 elementor-widget elementor-widget-heading\" data-id=\"bb69dd6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The scientific journal npj Digital Medicine, from Nature, published a guide to general concepts on the use of Artificial Intelligence (AI) in the medical sector, as well as definitions, functions and examples of its use.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a94561e elementor-section-boxed elementor-section-height-default elementor-section-height-default wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no\" data-id=\"a94561e\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-58b61e7\" data-id=\"58b61e7\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a59e1e8 elementor-widget elementor-widget-text-editor\" data-id=\"a59e1e8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Artificial Intelligence has gained great relevance over the past ten years. It is currently used in different industries, such as in the automotive or entertainment industry. Social media services use machine learning algorithms to function. In the field of medicine, the potential is greater, as it is possible to use it for drug development and design, for diagnostics, and for medical care, however, current evidence care has not been enough to further use.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3d76b56 elementor-section-boxed elementor-section-height-default elementor-section-height-default wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no\" data-id=\"3d76b56\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-8aae061\" data-id=\"8aae061\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b779b8f elementor-widget elementor-widget-image\" data-id=\"b779b8f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1200\" height=\"630\" src=\"https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature.png\" class=\"attachment-full size-full wp-image-10465\" alt=\"\" srcset=\"https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature.png 1200w, https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-660x347.png 660w, https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-768x403.png 768w, https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-840x441.png 840w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-94c2120\" data-id=\"94c2120\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0ab44f4 elementor-widget elementor-widget-text-editor\" data-id=\"0ab44f4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>\u201cThere is no doubt that A.I. will have a beneficial role in healthcare and can penetrate the boundaries of adoption only if medical professionals serve as knowledgeable and supportive guides and leaders in the process,\u201d the authors mention in the article.<\/p><p>To understand all the concepts involved in Artificial Intelligence, the authors developed a guide, which begins with the definition of AI, its levels and methods, as well as the differences between them. \u201cAI is an interdisciplinary field spanning computer science, psychology, linguistics, and philosophy, among others.\u201d The simplified definition is \u201cmachines that mimic the cognitive functions that humans associate with the human mind, such as learning and problem solving.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b38c0be elementor-section-boxed elementor-section-height-default elementor-section-height-default wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no\" data-id=\"b38c0be\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cff60e3\" data-id=\"cff60e3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6249bf6 elementor-widget elementor-widget-text-editor\" data-id=\"6249bf6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Nick Bostrom, a philosopher at Oxford University, defined the three main levels of AI in his book Superintelligence: <em>superintelligence<\/em>:<\/p><ul><li>Artificial Narrow Intelligence (ANI): It is an algorithm that can develop a defined task with high precision. It is usually used to solve problems of sorting and grouping text, voice or images.<\/li><li>Artificial General Intelligence (AGI): This type of intelligence could at some point have the same cognitive ability of a human being. Able to reason to discuss, memorize and solve problems.<\/li><li>Artificial Superintelligence (ASI): This level does not exist and could theoretically have more developed capabilities than a human.<\/li><\/ul><p>First, it is essential to know that AI works through<em>machine<\/em> <em>learning<\/em>, thanks to this technology, tasks that were previously complicated to perform within health care through traditional algorithms, now they are no more. This process involves providing the algorithm with a massive amount of data, and the algorithm through machine learning will create strategies to solve specific tasks.<\/p><p>There are also variations in machine learning:<\/p><ul><li>Supervised learning: It is used to define the task that the algorithm will learn based on data that we already know. For example, there are two sets of medical records of patients in a hospital, A and B. A contains a family history, laboratory or diagnostic results. B contains the same data, without diagnosis. So the ideal model to develop is one that learns to assign the right diagnoses to patients.<\/li><\/ul><ul><li>Unsupervised learning: It is a built model that, to follow rules, however, the algorithm learns by itself, and is not modified. It is used, for example, to group tissue samples based on similar genetic values, or even for drug development.<\/li><\/ul><ul><li>Reinforcement learning: This type of learning allows the algorithm to learn how to complete tasks without training. That is, the algorithm starts working alone, based on certain basic rules. An example cited by the authors is the following \"the authors used this method to determine the dosage of the clinical trial, where the algorithm learned the appropriate dosing regimen to reduce the mean diameters of tumors in patients undergoing chemo and radiation therapy\". This type of learning is directly applicable to health care.<\/li><\/ul><p>The exponential growth of machine learning, both in real applications and in studies and research, was thanks to the rise of AI, when artificial neural networks began to develop within this field. From 2005 to 2014, 6.747 studies on machine and deep learning were published in Pubmed.com, however, just in 2019, 12,563 were published and in 2020, until May, 5.542 studies were published, showing the importance of both machine and deep learning and AI in academics and research.<\/p><p>Both concepts respond to different problems and apply to different situations. For example, machine learning can be used as a substitute for traditional statistical models, as it can include a large number of variables while traditional analyses were designed to enter data in smaller numbers. In prediction models, for example, in epidemiology, trained algorithms are used to create prediction models in infectious diseases.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-86950e6 elementor-section-boxed elementor-section-height-default elementor-section-height-default wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no\" data-id=\"86950e6\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-e6ee25d\" data-id=\"e6ee25d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bc41629 elementor-widget elementor-widget-text-editor\" data-id=\"bc41629\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In the article this difference is explained through an example of medical records and specific patient conditions: \u201clet\u2019s build a model that can cluster patients by diagnosis based on the data in their medical records. If a medical record contains the expression Type 1 Diabetes, a machine learning model will learn to put all such patients in the Type 1 Diabetes cluster. But a deep learning algorithm could learn with time without human input that patients with medical records that only mention T1D should also be assigned to the same group. Programmers of other machine learning algorithms should add these alternatives themselves\u201d.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-95e8520\" data-id=\"95e8520\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0f2e7d0 elementor-widget elementor-widget-image\" data-id=\"0f2e7d0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1200\" height=\"630\" src=\"https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-int.png\" class=\"attachment-full size-full wp-image-10467\" alt=\"\" srcset=\"https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-int.png 1200w, https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-int-660x347.png 660w, https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-int-768x403.png 768w, https:\/\/saluddigital.com\/wp-content\/uploads\/2020\/10\/Gu\u00eda-sobre-aspectos-generales-y-complementarios-sobre-Inteligencia-Artificial-para-profesionales-de-la-salud-es-publicada-en-Nature-int-840x441.png 840w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-be45d75 elementor-section-boxed elementor-section-height-default elementor-section-height-default wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no\" data-id=\"be45d75\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e1f2180\" data-id=\"e1f2180\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-060c78a elementor-widget elementor-widget-text-editor\" data-id=\"060c78a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Both concepts respond to different problems and apply to different situations. For example, machine learning can be used as a substitute for traditional statistical models, as it can include a large number of variables while traditional analyses were designed to enter data in smaller numbers. In prediction models, for example, in epidemiology, trained algorithms are used to create prediction models in infectious diseases.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d2d376b elementor-section-boxed elementor-section-height-default elementor-section-height-default wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no\" data-id=\"d2d376b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-56c04f8\" data-id=\"56c04f8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fa4ac33 elementor-widget elementor-widget-toggle\" data-id=\"fa4ac33\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"toggle.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2621\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-2621\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2621\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-2621\"><p><strong>NATURE <\/strong><\/p><p><a href=\"https:\/\/www.nature.com\/articles\/s41746-020-00333-z\">https:\/\/www.nature.com\/articles\/s41746-020-00333-z<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>La revista cient\u00edfica npj Digital Medicine, perteneciente a Nature, public\u00f3 una gu\u00eda de conceptos generales sobre el uso de Inteligencia Artificial (IA) en el sector m\u00e9dico, as\u00ed como definiciones, funciones y ejemplos de su uso. La Inteligencia Artificial ha adquirido gran relevancia en los \u00faltimos diez a\u00f1os. Actualmente se utiliza en diferentes industrias, como en el sector automotriz o el entretenimiento. Los servicios de redes sociales utilizan algoritmos de aprendizaje autom\u00e1tico para funcionar. En el campo de la medicina, el potencial es mayor, ya que es posible utilizarla para el desarrollo y dise\u00f1o de f\u00e1rmacos, para diagn\u00f3sticos, y para atenci\u00f3n m\u00e9dica, sin embargo, la atenci\u00f3n evidencia actual no ha sido suficiente para masificar su uso. \u201cNo hay duda de que la IA tendr\u00e1 un papel beneficioso en el cuidado de la salud y puede traspasar los l\u00edmites de la adopci\u00f3n solo si los profesionales m\u00e9dicos sirven como gu\u00edas y l\u00edderes informados y solidarios en el proceso\u201d, mencionan los autores en el art\u00edculo. Para la comprensi\u00f3n de todos los conceptos que involucra la Inteligencia Artificial, los autores elaboraron una gu\u00eda, la cual comienza con la definici\u00f3n de IA, sus niveles y m\u00e9todos, as\u00ed como la diferencias entre estos. \u201cLa IA es un campo interdisciplinario que abarca la inform\u00e1tica, la psicolog\u00eda, la ling\u00fc\u00edstica y la filosof\u00eda, entre otros\u201d. La definici\u00f3n simplificada es \u201cm\u00e1quinas que imitan las funciones cognitivas que los humanos asocian con la mente humana, como el aprendizaje y la resoluci\u00f3n de problemas\u201d. Nick Bostrom, fil\u00f3sofo de la Universidad de Oxford defini\u00f3 los tres niveles principales de la IA en su libro Superintelligence: Inteligencia artificial estrecha (ANI): Es un algoritmo que puede desarrollar una tarea definida con alta precisi\u00f3n. Es usualmente utilizada para resolver problemas de clasificaci\u00f3n y agrupaci\u00f3n de texto, voz o im\u00e1genes. Inteligencia artificial general (AGI): Este tipo de inteligencia, podr\u00eda en alg\u00fan momento tener la misma capacidad cognitiva de un ser humano. Capaz de razonar discutir, memorizar y resolver problemas. Superinteligencia artificial (ASI): Este nivel no existe y de manera te\u00f3rica podr\u00eda tener capacidades m\u00e1s desarrolladas que un humano. Primero, es esencial conocer que la IA funciona a trav\u00e9s de aprendizaje autom\u00e1tico (machine learning), gracias a esta tecnolog\u00eda, tareas que antes eran complicadas de realizar dentro de la atenci\u00f3n m\u00e9dica a trav\u00e9s de los algoritmos tradicionales, ahora no lo son m\u00e1s. Este proceso implica dotar al algoritmo con una cantidad masiva de datos, y el algoritmo a trav\u00e9s de aprendizaje autom\u00e1tico crear\u00e1 estrategias para resolver tareas espec\u00edficas. Tambi\u00e9n existes variaciones en el aprendizaje autom\u00e1tico: Aprendizaje supervisado: Es utilizado para definir la tarea que aprender\u00e1 el algoritmo en funci\u00f3n de datos que ya conocemos. Por ejemplo, en un hospital hay dos conjuntos de registros m\u00e9dicos de pacientes, A y B. El A contiene antecedentes familiares, resultados de laboratorio o de diagn\u00f3sticos. El B contiene los mismos datos, sin el diagn\u00f3stico. Por lo que el modelo ideal a desarrollar es aquel que aprenda a asignar los diagn\u00f3sticos correctos a los pacientes. Aprendizaje sin supervisi\u00f3n: Es un modelo construido que, para seguir reglas, sin embargo, el algoritmo aprende por s\u00ed mismo, y no se modifica. Es utilizado, por ejemplo, para agrupar muestras de tejido bas\u00e1ndose en valores gen\u00e9ticos similares, o incluso para el desarrollo de f\u00e1rmacos. Aprendizaje reforzado: Este tipo de aprendizaje permite que el algoritmo aprenda a completar tareas sin necesidad de entrenamiento. Es decir, el algoritmo comienza a trabajar solo, tomando como base ciertas reglas b\u00e1sicas. Un ejemplo citado por los autores es el siguiente &#8220;los autores utilizaron este m\u00e9todo para determinar la dosificaci\u00f3n del ensayo cl\u00ednico, donde el algoritmo aprendi\u00f3 el r\u00e9gimen de dosificaci\u00f3n apropiado para reducir los di\u00e1metros medios de los tumores en pacientes sometidos a quimio y radioterapia&#8221;. Este tipo de aprendizaje es directamente aplicable a la atenci\u00f3n m\u00e9dica. El crecimiento exponencial del aprendizaje autom\u00e1tico, tanto en aplicaciones reales como en estudios e investigaciones, se dio gracias al auge de la IA, cuando se comenzaron a desarrollar redes neuronales artificiales dentro de este ramo. Del 2005 al 2014 fueron publicados 6 mil 747 estudios acerca de aprendizaje autom\u00e1tico y profundo en Pubmed.com, sin embargo, solamente en 2019 fueron publicados 12 mil 563 y en 2020 han sido publicados hasta mayo 5 mil 542, lo que muestra la importancia que han ganado tanto el aprendizaje autom\u00e1tico y profundo como la IA en el \u00e1mbito acad\u00e9mico y de investigaci\u00f3n. Por otra parte, el aprendizaje profundo, es un subconjunto del aprendizaje autom\u00e1tico, ambos comparten funciones sin embargo tienen capacidades distintas. \u201cEl aprendizaje profundo utiliza una estructura en capas de redes neuronales artificiales que se inspira en la red neuronal del cerebro humano. La estructura interna y el n\u00famero de capas dentro de una red neuronal es un campo de investigaci\u00f3n activa, pero como regla general podemos decir que una red m\u00e1s profunda con m\u00e1s capas puede aprender tareas m\u00e1s complejas, al mismo tiempo que requiere m\u00e1s datos y m\u00e1s tiempo\u201d. En el art\u00edculo esta diferencia es explicada a trav\u00e9s de un ejemplo de registros m\u00e9dicos y condiciones espec\u00edficas de los pacientes: \u201cconstruyamos un modelo que pueda agrupar a los pacientes por diagn\u00f3stico en funci\u00f3n de los datos de sus registros m\u00e9dicos. Si un registro m\u00e9dico contiene la expresi\u00f3n Diabetes tipo 1, un modelo de aprendizaje autom\u00e1tico aprender\u00e1 a colocar a todos esos pacientes en el grupo de Diabetes tipo 1. Pero un algoritmo de aprendizaje profundo podr\u00eda aprender con el tiempo sin intervenci\u00f3n humana que los pacientes con registros m\u00e9dicos que solo mencionan la diabetes Tipo 1 tambi\u00e9n deben ser asignados al mismo grupo. Los programadores de otros algoritmos de aprendizaje autom\u00e1tico deber\u00edan agregar estas alternativas ellos mismos\u201d. Ambos conceptos responden a diferentes problemas y se aplican a situaciones distintas. Por ejemplo, el aprendizaje autom\u00e1tico, puede ser utilizados como sustituto de modelos estad\u00edsticos tradicionales, ya que puede incluir un gran n\u00famero de variables mientras que los an\u00e1lisis tradicionales fueron dise\u00f1ados para ingresar datos en menor cantidad. En modelos de predicci\u00f3n, por ejemplo, en epidemiolog\u00eda, algoritmos entrenados son utilizados para crear modelos de predicci\u00f3n en enfermedades infecciosas. BIBLIOGRAF\u00cdA NATURE &#8230; <a title=\"Guide to General and Complementary Aspects on Artificial Intelligence for Health Professionals was published on Nature\" class=\"read-more\" href=\"https:\/\/saluddigital.com\/en\/comunidades-conectadas\/guia-sobre-aspectos-generales-y-complementarios-sobre-inteligencia-artificial-para-profesionales-de-la-salud-es-publicada-en-nature\/\" aria-label=\"Read more about Gu\u00eda sobre aspectos generales y complementarios sobre Inteligencia Artificial para profesionales de la salud es publicada en Nature\">Read more<\/a><\/p>","protected":false},"author":1,"featured_media":10465,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,154,1418],"tags":[145],"class_list":["post-10464","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-comunidades-conectadas","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/10464","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=10464"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/10464\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/10465"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=10464"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=10464"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=10464"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}