Will machine learning end the viability of radiology as a thriving medical specialty?

Will machine learning end the viability of radiology as a thriving medical specialty? Br J Radiol. 2018 Oct 16;:20180416 Authors: Chan S, Siegel EL Abstract There have been tremendous advances in artificial intelligence and machine learning within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in pictures have led some individuals, including computer science experts and health care system experts in machine learning, to make predictions that machine learning algorithms will soon lead to the replacement of the radiologist. However, there are complex technological, regulatory, and medicolegal obstacles facing the implementation of machine learning in radiology that will definitely preclude replacement of the radiologist by these algorithms within the next two decades and beyond. While not a comprehensive review of machine learning, this article is intended to highlight specific features of machine learning where it faces significant technological and health care systems challenges. Rather than replacing radiologists, machine learning will provide quantitative tools that will increase the value of diagnostic imaging as a biomarker, increase image quality with decreased acquisition times, and improve...
Source: The British Journal of Radiology - Category: Radiology Authors: Tags: Br J Radiol Source Type: research