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Eur Radiol. 2020 Feb 17. doi: 10.1007/s00330-020-06672-5. [Epub ahead of print]

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.

Author information

1
Department of Radiology, New York University Robert I Grossman School of Medicine, New York, NY, USA. Michael.Recht@nyulangone.org.
2
Charité - Universitätsmedizin Berlin, Humboldt-Universität and Freie Universität zu Berlin, Berlin, Germany.
3
Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
4
Department of Radiology and Biomedical Informatics, Stanford University, Palo Alto, CA, USA.
5
Department of Radiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
6
Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.
7
Department of Political Science, University of Vienna, Vienna, Austria.
8
Department of Global Health & Social Medicine, King's College, London, UK.
9
Hogan Lovells US LLP, Washington, D.C., USA.

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.

KEYWORDS:

Artificial intelligence; Bioethics; Data; Education; Regulation

PMID:
32064565
DOI:
10.1007/s00330-020-06672-5

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