Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

• Published in: Radiologia brasileira Vol. 52; no. 6; pp. 387 - 396
• Main Authors: Santos, Marcel Koenigkam, Ferreira Júnior, José Raniery, Wada, Danilo Tadao, Tenório, Ariane Priscilla Magalhães, Barbosa, Marcello Henrique Nogueira, Marques, Paulo Mazzoncini de Azevedo
• Format: Journal Article
• Language: English
• Published: Brazil Colégio Brasileiro de Radiologia e Diagnóstico por Imagem 01.11.2019; Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem
• Subjects: RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING; Review; Computer aided diagnosis; Artificial intelligence; Machine learning; Radiomics; Radiômica; Inteligência artificial; Aprendizado de máquina; Diagnóstico auxiliado por computador
• ISSN: 0100-3984; 1678-7099; 1678-7099
• DOI: 10.1590/0100-3984.2019.0049

The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to "know everything about all exams and regions". In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, "big data", and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.