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Acad Med ; 96(7): 954-957, 2021 07 01.
Artículo en Inglés | MEDLINE | ID: covidwho-1364834

RESUMEN

Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the health care system, but most are not equipped to make informed decisions regarding deployment and application of ML technologies in patient care. It is of paramount importance that ML concepts are integrated into medical curricula to position physicians to become informed consumers of the emerging tools employing ML. This paradigm shift is similar to the evidence-based medicine (EBM) movement of the 1990s. At that time, EBM was a novel concept; now, EBM is considered an essential component of medical curricula and critical to the provision of high-quality patient care. ML has the potential to have a similar, if not greater, impact on the practice of medicine. As this technology continues its inexorable march forward, educators must continue to evaluate medical curricula to ensure that physicians are trained to be informed stakeholders in the health care of tomorrow.


Asunto(s)
Atención a la Salud/organización & administración , Educación Médica/métodos , Medicina Basada en la Evidencia/historia , Aprendizaje Automático/estadística & datos numéricos , Anciano , Algoritmos , Prueba de COVID-19/instrumentación , Toma de Decisiones Clínicas/ética , Ensayos Clínicos como Asunto , Curriculum/estadística & datos numéricos , Atención a la Salud/estadística & datos numéricos , Retinopatía Diabética/diagnóstico , Diagnóstico por Imagen/instrumentación , Femenino , Historia del Siglo XX , Humanos , Responsabilidad Legal , Masculino , Relaciones Médico-Paciente/ética , Médicos/organización & administración , Participación de los Interesados , Estados Unidos , United States Food and Drug Administration/legislación & jurisprudencia
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