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Vol. 64. Issue 3.
Pages 228-236 (May - June 2022)
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Vol. 64. Issue 3.
Pages 228-236 (May - June 2022)
Update in Radiology
Artificial Intelligence in Radiology: an introduction to the most important concepts
Inteligencia artificial en Radiología: introducción a los conceptos más importantes
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162
A. Pérez del Barrioa,
Corresponding author
amaia.pdb@gmail.com

Corresponding author.
, P. Menéndez Fernández-Mirandaa, P. Sanz Bellóna, L. Lloret Iglesiasb, D. Rodríguez Gonzálezb
a Servicio de Radiodiagnóstico, Hospital Universitario Marqués de Valdecilla, Santander, Cantabria, Spain
b Instituto de Física de Cantabria, Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Cantabria, Santander, Cantabria, Spain
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Abstract

The interpretation of medical imaging tests is one of the main tasks that radiologists do. For years, it has been a challenge to teach computers to do this kind of cognitive task; the main objective of the field of computer vision is to overcome this challenge. Thanks to technological advances, we are now closer than ever to achieving this goal, and radiologists need to become involved in this effort to guarantee that the patient remains at the center of medical practice.

This article clearly explains the most important theoretical concepts in this area and the main problems or challenges at the present time; moreover, it provides practical information about the development of an artificial intelligence project in a radiology department.

Keywords:
Artificial intelligence
Radiology
Machine learning
Deep learning
Artificial neural networks
Medical imaging
Resumen

La interpretación de la imagen médica es una de las principales tareas que realiza el radiólogo. Conseguir que los ordenadores sean capaces de realizar este tipo de tareas cognitivas ha sido, durante años, un reto y a la vez un objetivo en el campo de la visión artificial. Gracias a los avances tecnológicos estamos ahora más cerca que nunca de conseguirlo y los radiólogos debemos involucrarnos en ello para garantizar que el paciente siga siendo el centro de la práctica médica.

Este artículo explica de forma clara los conceptos teóricos más importantes de esta área y los principales problemas o retos actuales; además, aporta información práctica en relación con el desarrollo de un proyecto de inteligencia artificial en un servicio de Radiología.

Palabras clave:
Inteligencia artificial
Radiología
Aprendizaje automático
Aprendizaje profundo
Redes neuronales artificiales
Imagen médica

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