Through the agreement with The British Institute of Radiology, the next resource for our members is now available. This is a free service for members and will be available until 21 May 2019.

Simply click on the link and register and set your own password. If you are already registered, please log in with your email address and password. Then access the presentation!

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

Stephen Chan MD, Department of Radiology, Harlem Hospital Center and Columbia University, New York, NY, USA and

Eliot Sigel, Department of Diagnostic Radiology and Nuclear Medicine, VA Maryland Health Care System, Baltimore, MD, USA

There have been tremendous advances in artificial intelligence (AI) and machine learning (ML) 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 ML 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 which face 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 workflow, communication, and patient safety. In the foreseeable future, we predict that today’s generation of radiologists will be replaced not by ML algorithms, but by a new breed of data science-savvy radiologists who have embraced and harnessed the incredible potential that machine learning has to advance our ability to care for our patients. In this way, radiology will remain a viable medical specialty for years to come.

The BIR World Partner Network Scheme