{"id":13172,"date":"2021-03-26T09:32:19","date_gmt":"2021-03-26T15:32:19","guid":{"rendered":"https:\/\/saluddigital.com\/?p=13172"},"modified":"2025-10-21T12:53:31","modified_gmt":"2025-10-21T18:53:31","slug":"investigadores-del-mit-desarrollan-sistema-de-inteligencia-artificial-para-detectar-errores-en-la-autoadministracion-de-tratamientos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/inteligencia-artificial-y-ciencia\/investigadores-del-mit-desarrollan-sistema-de-inteligencia-artificial-para-detectar-errores-en-la-autoadministracion-de-tratamientos\/","title":{"rendered":"MIT researchers develop AI system to detect errors in self-administered treatments"},"content":{"rendered":"
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MIT researchers developed an Artificial Intelligence (AI) device to detect errors in the self-administration of medical treatments, such as the application of insulin or the use of inhalers. <\/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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Poor self-administration of medical treatment is a real public health problem that can cause serious health problems in patients, such as hospitalizations. However, it can be prevented through proper adherence to treatment by patients. MIT researchers developed a system that uses Artificial Intelligence to avoid this kind of situations in the self-administration of drugs specifically through devices such as inhalers or insulin pens.<\/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 study \u201cAssessment of medication self-administration using artificial intelligence\u201d, published in Nature details that: \u201cErrors in medication self-administration (MSA) lead to poor treatment adherence, increased hospitalizations and higher healthcare costs\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

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These errors are particularly common when they involve the use of inhalers or insulin pens. That's why MIT researchers presented a non-contact AI model that achieves MSA error detection and monitoring.<\/p>

\u201cThe system was developed by observing self-administration conducted by volunteers and evaluated by comparing its prediction with human annotations. Findings from this study demonstrate that our approach can automatically detect when patients use their inhalers (area under the curve (AUC)\u2009=\u20090.992) or insulin pens (AUC\u2009=\u20090.967), and assess whether patients follow the appropriate steps for using these devices (AUC\u2009=\u20090.952),\u201d the study explains.<\/p>

The model combines wireless technology and AI, and can be installed at home in the shape of a sensor to alert not only patients and their relatives, but also their physician, about possible medication errors. In this way they seek to reduce avoidable hospitalizations.<\/p>

The system works as described below. First, the sensor tracks the patient's movements within a radius of approximately 10 meters, then the AI tracks the signs to distinguish whether the patient is using an insulin pen or an inhaler. Finally, the system alerts the patient and the medical professional when it has detected an error in the patient's self-administration.<\/p>

The system has been tested and validated through a large set of more than 47,000 examples of self-administration events in 107 participants. In addition, the study used different physical environments, such as offices, living rooms, kitchens, hallways to measure the tool.<\/p>

You can learn more about the results by reading the full article through the following link: https:\/\/www.nature.com\/articles\/s41591-021-01273-1<\/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

NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41591-021-01273-1<\/a><\/p>

\u00a0<\/p>

AI MEAS<\/strong><\/p>

https:\/\/ai-med.io\/more-news\/new-ai-system-detects-errors-when-patients-self-medicate\/<\/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":"

Investigadores del MIT desarrollaron un dispositivo de Inteligencia Artificial (IA), para la detecci\u00f3n de errores en la autoadministraci\u00f3n de tratamientos m\u00e9dicos, como la aplicaci\u00f3n de insulina o el uso de inhaladores. La mala autoadministraci\u00f3n de un tratamiento m\u00e9dico es un problema real de salud p\u00fablica que puede ocasionar problemas graves de salud en los pacientes, como hospitalizaciones. Sin embargo, se puede prevenir a trav\u00e9s de una adherencia adecuada al tratamiento de parte de los pacientes. Investigadores del MIT desarrollaron un sistema que utiliza Inteligencia Artificial, para evitar este tipo de situaciones en la autoadministraci\u00f3n de medicamentos espec\u00edficamente a trav\u00e9s de dispositivos como inhaladores o plumas de insulina. El estudio “Evaluaci\u00f3n de la autoadministraci\u00f3n de medicamentos mediante inteligencia artificial”, publicado en Nature detalla que: “Los errores en la autoadministraci\u00f3n de medicamentos (MSA, por sus siglas en ingl\u00e9s) conducen a una mala adherencia al tratamiento, aumento de las hospitalizaciones y mayores costos de atenci\u00f3n m\u00e9dica”. Este tipo de errores son particularmente comunes cuando se involucra el uso de inhaladores o plumas de insulina. Es por ello que investigadores del MIT presentaron un modelo de IA sin contacto que logra la detecci\u00f3n y monitoreo de errores en la MSA. \u201cEl sistema se desarroll\u00f3 observando la autoadministraci\u00f3n realizada por voluntarios y se evalu\u00f3 comparando su predicci\u00f3n con anotaciones humanas. Los resultados de este estudio demuestran que nuestro enfoque puede detectar autom\u00e1ticamente cuando los pacientes usan sus inhaladores (\u00e1rea bajo la curva (AUC) = 0,992) o plumas de insulina (AUC = 0,967), y evaluar si los pacientes siguen los pasos adecuados para utilizar estos dispositivos (AUC = 0,952)\u201d, explica el estudio. El modelo combina tecnolog\u00eda inal\u00e1mbrica e IA, y puede instalarse en forma de sensor en los hogares de los pacientes para alertar sobre posibles errores de medicaci\u00f3n, no solo al mismo paciente y sus familiares sino a su m\u00e9dico. De esta forma buscan reducir hospitalizaciones que pueden evitarse. El sistema funciona de la siguiente manera, en primera instancia el sensor rastrea los movimientos del paciente en un radio de aproximadamente 10 metros, posteriormente la IA rastrea los signos para distinguir si el paciente utiliza una pluma de insulina o un inhalador. Finalmente, el sistema alerta al paciente y al profesional m\u00e9dicos cuando haya detectado un error en la autoadministraci\u00f3n del paciente. El sistema ha sido probado y validado a trav\u00e9s de un gran conjunto de m\u00e1s de 47 mil ejemplos de eventos de autoadministraci\u00f3n en 107 participantes. Adem\u00e1s, el estudio utiliz\u00f3 diferentes entornos f\u00edsicos, como oficinas, salones, cocinas, pasillos para medir la herramienta. Puedes conocer m\u00e1s sobre los resultados leyendo el art\u00edculo completo a trav\u00e9s del siguiente enlace: https:\/\/www.nature.com\/articles\/s41591-021-01273-1 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41591-021-01273-1 \u00a0 AI MED https:\/\/ai-med.io\/more-news\/new-ai-system-detects-errors-when-patients-self-medicate\/<\/p>","protected":false},"author":1,"featured_media":13173,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3395,3393],"tags":[145],"class_list":["post-13172","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apps-moviles-e-internet-de-las-cosas","category-inteligencia-artificial-y-ciencia","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13172","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=13172"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13172\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/13173"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=13172"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=13172"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=13172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}