Abstract
Abstract: Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. Key Points: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.
| Original language | English |
|---|---|
| Pages (from-to) | 3576-3584 |
| Number of pages | 9 |
| Journal | European Radiology |
| Volume | 30 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jun 2020 |
Bibliographical note
Funding Information:Prof. Dewey has received grant support from the FP7 Program of the European Commission for the randomized multicenter DISCHARGE trial (603266-2, HEALTH-2012.2.4.-2). He also received grant support from German Research Foundation (DFG) in the Heisenberg Program (DE 1361/14-1); from graduate program on quantitative biomedical imaging (BIOQIC, GRK 2260/1); for fractal analysis of myocardial perfusion (DE 1361/19-1); from the Priority Programme Radiomics (DE 1361/19-1 and 20-1 in SPP 2177/1). He also received funding from the Berlin University Alliance and from the Digital Health Accelerator of the Berlin Institute of Health. Prof. Dewey has received lecture fees from Canon, Guerbet, Cardiac MR Academy Berlin, and Bayer. Prof. Dewey is also the editor of Cardiac CT , published by Springer, and offers hands-on courses on CT imaging ( www.ct-kurs.de ). Institutional master research agreements exist with Siemens, General Electric, Philips, and Canon. The terms of these arrangements are managed by the legal department of Charité – Universitätsmedizin Berlin. Professor Dewey holds a joint patent with Florian Michallek on dynamic perfusion analysis using fractal analysis (PCT/EP2016/071551).
Funding Information:
Dr. Langlotz reports non-financial support from Nines.ai; personal fees and non-financial support from whiterabbit.ai; non-financial support from Galileo CDS, Inc.; non-financial support from Bunker Hill, Inc.; grants from GE Healthcare; grants from Philips Healthcare; and grants from Siemens Healthineers, outside the submitted work.
Publisher Copyright:
© 2020, European Society of Radiology.