There is a newer version of the record available.

Published April 3, 2020 | Version v1
Journal article

Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists

  • 1. Università Degli Studi di Milano. Postgraduation School in Radiodiagnostics (Italy)
  • 2. King's College Hospital. Interventional Radiology (United Kingdom)
  • 3. San Paolo Hospital, University of Milan. Diagnostic and Interventional Radiology Department (Italy)
  • 4. UOC Radiologia, ASST Rhodense (Italy)
  • 5. E.O. Galliera Hospital. Department of Diagnostic Imaging, Interventional Radiology Unit (Italy)
  • 6. Università Degli Studi di Milano. Dipartimento Di Scienze Della Salute (Italy)
  • 7. Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico. Radiology Department (Italy)

Description

Artificial intelligence (AI) is revolutionizing healthcare and transforming the clinical practice of physicians across the world. Radiology has a strong affinity for machine learning and is at the forefront of the paradigm shift, as machines compete with humans for cognitive abilities. AI is a computer science simulation of the human mind that utilizes algorithms based on collective human knowledge and the best available evidence to process various forms of inputs and deliver desired outcomes, such as clinical diagnoses and optimal treatment options. Despite the overwhelmingly positive uptake of the technology, warnings have been published about the potential dangers of AI. Concerns have been expressed reflecting opinions that future medicine based on AI will render radiologists irrelevant. Thus, how much of this is based on reality? To answer these questions, it is important to examine the facts, clarify where AI really stands and why many of these speculations are untrue. We aim to debunk the 6 top myths regarding AI in the future of radiologists.

Additional details

Identifiers

Publishing Information

Journal Title
Medical Oncology (Online)
Journal Volume
37
Journal Issue
5
Journal Page Range
vp.
ISSN
1559-131X

Optional Information

Copyright
Copyright (c) 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020