{"id":24388,"date":"2022-03-15T08:59:19","date_gmt":"2022-03-15T14:59:19","guid":{"rendered":"https:\/\/saluddigital.com\/?p=24388"},"modified":"2025-10-21T10:09:13","modified_gmt":"2025-10-21T16:09:13","slug":"ecosistema-como-servicio-los-avances-de-la-inteligencia-artificial-en-el-campo-clinico","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/ecosistema-como-servicio-los-avances-de-la-inteligencia-artificial-en-el-campo-clinico\/","title":{"rendered":"Ecosystem as a service: advances in artificial intelligence in the clinical field"},"content":{"rendered":"
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Article published in PLOS Digital Health shows the importance of developing Artificial Intelligence (AI) algorithms, focused on frontline health care services.<\/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
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Various studies have shown that machine learning and AI in clinical settings are tools to improve the prevention and diagnosis of diseases. However, there are a number of challenges and challenges related to the development of algorithms, especially related to the lack of transparency in their creation process.<\/p>

In this sense, the Critical Data Consortium of the Massachusetts Institute of Technology (MIT-CD), specialists in the investigation of data related to human health, developed a platform for education and accountability on algorithms. The so-called Ecosystem as a Service (EaaS) approach aims at accountability and collaboration between clinical and technical experts for the promotion of AI in the clinical field.<\/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

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"The EaaS approach provides a variety of resources, from open source databases and specialized human resources to networking and collaboration opportunities," the authors explain. Specialists hope that these tools will promote further exploration and expansion of the EaaS approach. They also explain that the development of AI in clinical practice will promote equitable access to medical care.<\/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

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\u201cMIT-CD's initial EaaS approach implementation efforts have shown promising results in improving access to healthcare and clinical care through new and refined approaches to interpreting data in conjunction with partner universities and research laboratories around the world. the world,\u201d the article explains.<\/p>

In this way, the EaaS approach looks like a sustainable and also profitable option for the implementation of AI in health and specifically in the clinical field. However, its massive adoption still faces various obstacles related to collaboration, cost structure or data sharing.<\/p>

You can read the full article at the following link:<\/p>

https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000011<\/a><\/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

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/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 BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

PLOS DIGITAL HEALTH<\/strong><\/p>

https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000011<\/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":"

Art\u00edculo publicado en PLOS Digital Health muestra la importancia de desarrollar algoritmos de Inteligencia Artificial (IA), enfocados en los servicios de primera l\u00ednea de atenci\u00f3n m\u00e9dica. Diversos estudios han demostrado que el aprendizaje autom\u00e1tico y la IA en entornos cl\u00ednicos, son herramientas para mejorar la prevenci\u00f3n y diagn\u00f3stico de enfermedades. No obstante, hay una serie de retos y desaf\u00edos relacionados con el desarrollo de algoritmos, especialmente relacionados con la falta de transparencia en su proceso de creaci\u00f3n. En este sentido el Consorcio de Datos Cr\u00edticos del Instituto de Tecnolog\u00eda de Massachusetts (MIT-CD), especialistas en la investigaci\u00f3n de datos relacionados con la salud humana, desarrollaron una plataforma de educaci\u00f3n y rendici\u00f3n de cuentas sobre algoritmos. El enfoque llamado Ecosistema como servicio (EaaS, en ingl\u00e9s), tiene como objetivo la rendici\u00f3n de cuentas y la colaboraci\u00f3n entre expertos cl\u00ednicos y t\u00e9cnicos para la promoci\u00f3n de la IA en el campo cl\u00ednico. \u201cEl enfoque EaaS proporciona una variedad de recursos, desde bases de datos de c\u00f3digo abierto y recursos humanos especializados hasta oportunidades de creaci\u00f3n de redes y colaboraci\u00f3n\u201d, explican los autores. Los especialistas esperan que estas herramientas logren la promoci\u00f3n de una mayor exploraci\u00f3n y expansi\u00f3n del enfoque EaaS.  Asimismo, explican que el desarrollo de IA en la pr\u00e1ctica cl\u00ednica, impulsar\u00e1 el acceso equitativo a la atenci\u00f3n m\u00e9dica. \u201cLos esfuerzos iniciales de implementaci\u00f3n del enfoque EaaS del MIT-CD han mostrado resultados prometedores en la mejora del acceso a la atenci\u00f3n m\u00e9dica y la atenci\u00f3n cl\u00ednica a trav\u00e9s de enfoques nuevos y refinados para interpretar datos junto con universidades asociadas y laboratorios de investigaci\u00f3n en todo el mundo\u201d, explica el art\u00edculo. De esta forma, el enfoque EaaS, luce como una opci\u00f3n sostenible y adem\u00e1s rentable para la implementaci\u00f3n de la IA en salud y espec\u00edficamente en el campo cl\u00ednico. Sin embargo, su adopci\u00f3n de forma masiva aun enfrenta diversos obst\u00e1culos relacionados con la colaboraci\u00f3n, la estructura de los costos o el intercambio de datos. Puedes leer el art\u00edculo completo en el siguiente enlace: https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000011 BIBLIOGRAF\u00cdA PLOS DIGITAL HEALTH https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000011<\/p>","protected":false},"author":1,"featured_media":24390,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-24388","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24388","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=24388"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24388\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/24390"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=24388"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=24388"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=24388"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}