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

Abstract 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.

摘要
近年来，放射学与诊断影像学学科发展日新月异。当前，影像学检查量呈指数级增长、医学亚专科分化不断加剧，同时各类成像方法的准确率持续提升，这使得放射科医师难以做到“通晓所有检查项目与影像解剖区域的相关知识”。此外，影像学检查已不再局限于定性诊断功能，如今还可提供疾病严重程度的定量信息，并能识别与预后及治疗反应相关的生物标志物。
鉴于上述背景，计算机辅助诊断（computer-aided diagnosis, CAD）系统应运而生，其研发目标为辅助诊断影像学工作，并助力临床治疗决策流程。随着人工智能（artificial intelligence, AI）、“大数据”与机器学习技术的发展，这些工具在医师日常诊疗工作中的应用正快速扩张，不仅推动了“每一位患者均为独特个体”的诊疗理念落地，也引领放射学走向多学科协作与精准医学的发展范式。
本文将介绍当前主流的影像分析计算工具及其分析原理，涵盖相关核心术语与概念，并探讨人工智能的发展对放射学与诊断影像学领域产生的影响。