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Titulo Artículo:
Artificial intelligence for precision education in radiology
Resumen:
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
Fecha de publicación:
2019.
Autores :
Andreas M. Rauschecker;
Jeffrey D. Rudie;
Po-Hao Chen;
Tessa S. Cook;
R. Nick Bryan;
Suyash Mohan;
Michael Tran Duong;
Autor corporativo:
The British journal of radiology,
Editores:
Medline-PubMed ;
Idioma:
Inglés
Páginas:
1
ISBN:
1748-880X
Existencias:
11
Palabras claves:
Educación médica
Artificial Intelligence
Radiology
Público objetivo:
Docentes
Medicos
Otros profesionales de la salud
Titulo Artículo:
Artificial intelligence for precision education in radiology
Resumen:
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
Fecha de publicación:
2019.
Autores :
Andreas M. Rauschecker;
Jeffrey D. Rudie;
Po-Hao Chen;
Tessa S. Cook;
R. Nick Bryan;
Suyash Mohan;
Michael Tran Duong;
Autor corporativo:
The British journal of radiology,
Editores:
Medline-PubMed ;
Idioma:
Inglés
Páginas:
1
Existencias:
11
Palabras claves:
Educación médica
Artificial Intelligence
Radiology
Público objetivo:
Docentes
Medicos
Otros profesionales de la salud
Titulo Artículo:
Artificial intelligence for precision education in radiology
Resumen:
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
Autores:
Andreas M. Rauschecker
,
Jeffrey D. Rudie
,
Po-Hao Chen
,
Tessa S. Cook
,
R. Nick Bryan
,
Suyash Mohan
,
Michael Tran Duong
,
.
Titulo Revista:
The British journal of radiology,
.
Numero:
Volumen:
92
Fecha de publicación:
2019.
Base de Datos Bibliográfica:
Medline-PubMed ,
.
Suplemento:
Idioma:
Inglés
Página Inicial:
1
Página Final:
11
ISBN:
1748-880X
Palabras claves:
Educación médica
Artificial Intelligence
Radiology
Público objetivo:
Docentes
Medicos
Otros profesionales de la salud
Título Medline-PubMed :
Artificial intelligence for precision education in radiology
Resumen:
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
Autores :
Andreas M. Rauschecker;
Jeffrey D. Rudie;
Po-Hao Chen;
Tessa S. Cook;
R. Nick Bryan;
Suyash Mohan;
Michael Tran Duong;
Autor corporativo:
The British journal of radiology,
Fecha de publicación:
2019.
Tipo :
Medline-PubMed .
Idioma:
Inglés
Palabras claves:
Educación médica
Artificial Intelligence
Radiology
Público objetivo:
Docentes
Medicos
Otros profesionales de la salud
Título Medline-PubMed :
Artificial intelligence for precision education in radiology
Resumen:
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
Autores :
Andreas M. Rauschecker;
Jeffrey D. Rudie;
Po-Hao Chen;
Tessa S. Cook;
R. Nick Bryan;
Suyash Mohan;
Michael Tran Duong;
Autor corporativo:
The British journal of radiology,
Fecha de publicación:
2019.
Paginas:
1.
ISBN:
1748-880X.
Idioma:
Inglés
Palabras claves:
Educación médica
Artificial Intelligence
Radiology
Público objetivo:
Docentes
Medicos
Otros profesionales de la salud
Titulo Artículo:
Artificial intelligence for precision education in radiology
Resumen:
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
Fecha de publicación:
2019.
Autor corporativo:
The British journal of radiology,
.
Idioma:
Inglés
Palabras claves:
Educación médica
Artificial Intelligence
Radiology
Público objetivo:
Docentes
Medicos
Otros profesionales de la salud
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Andreas M. Rauschecker Jeffrey D. Rudie Po-Hao Chen Tessa S. Cook R. Nick Bryan Suyash Mohan Michael Tran Duong Andreas M. Rauschecker Jeffrey D. Rudie Po-Hao Chen Tessa S. Cook R. Nick Bryan Suyash Mohan Michael Tran Duong Artificial intelligence for precision education in radiology. 2019; 92Ed. 1.
