Skip to main content
Top
Published in: European Journal of Nuclear Medicine and Molecular Imaging 4/2020

01-04-2020 | Artificial Intelligence | Editorial

Ethical principles for the application of artificial intelligence (AI) in nuclear medicine

Authors: Geoff Currie, K Elizabeth Hawk, Eric M. Rohren

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 4/2020

Login to get access

Excerpt

The emergence of artificial intelligence (AI) in Nuclear Medicine, and the promise of synthetic intelligence, heralds an era of disruptive technology with the potential to re-invigorate the ecosystem of Nuclear Medicine and reengineer the landscape in which Nuclear Medicine is practiced. While AI is not new in Nuclear Medicine, more recent developments and applications of machine learning and deep learning create refreshed interest in the ethical issues associated with AI implementation in Nuclear Medicine. Insight into the architecture, operation and implementation of AI in Nuclear Medicine is beyond the scope of this discussion and has been reported elsewhere (13). Nonetheless, it is important to provide key definitions. …
Literature
1.
go back to reference Currie G. Intelligent imaging: artificial intelligence augmented nuclear medicine. Journal of Nuclear Medicine Technology. 2019;47:217–22.CrossRef Currie G. Intelligent imaging: artificial intelligence augmented nuclear medicine. Journal of Nuclear Medicine Technology. 2019;47:217–22.CrossRef
2.
go back to reference Currie G. Intelligent Imaging: anatomy of machine learning and deep learning. Journal of Nuclear Medicine Technology. 2019;47(4):273–81.CrossRef Currie G. Intelligent Imaging: anatomy of machine learning and deep learning. Journal of Nuclear Medicine Technology. 2019;47(4):273–81.CrossRef
3.
go back to reference Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine learning and deep learning in medical imaging: intelligent imaging. Journal of Medical Imaging and Radiation Sciences. 2019;50(4):477–87.CrossRef Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine learning and deep learning in medical imaging: intelligent imaging. Journal of Medical Imaging and Radiation Sciences. 2019;50(4):477–87.CrossRef
4.
go back to reference Hamlet P, Tremblay J. Artificial intelligence in medicine. Metabolism clinical and experimental. 2017;69:S36–40.CrossRef Hamlet P, Tremblay J. Artificial intelligence in medicine. Metabolism clinical and experimental. 2017;69:S36–40.CrossRef
5.
go back to reference SFR-IA Group, CERF. Artificial intelligence and medical imaging 2018: French radiology community white paper. Diagn Interv Radiol. 2018;99:727–42. SFR-IA Group, CERF. Artificial intelligence and medical imaging 2018: French radiology community white paper. Diagn Interv Radiol. 2018;99:727–42.
7.
go back to reference Jalal S, Nicolaou S, Parker W. Artificial intelligence, radiology, and the way forward. Can Assoc Radiol J. 2019;70:10–2.CrossRef Jalal S, Nicolaou S, Parker W. Artificial intelligence, radiology, and the way forward. Can Assoc Radiol J. 2019;70:10–2.CrossRef
8.
go back to reference Balthazar P, Harri P, Prater A, Safdar NM. Protecting your patients' interests in the era of big data, artificial intelligence, and predictive analytics. J Am Coll Radiol. 2018;153:580–6.CrossRef Balthazar P, Harri P, Prater A, Safdar NM. Protecting your patients' interests in the era of big data, artificial intelligence, and predictive analytics. J Am Coll Radiol. 2018;153:580–6.CrossRef
9.
go back to reference Jaremko JL, Azar M, Bromwich R, et al. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J. 2019;70:107–18.CrossRef Jaremko JL, Azar M, Bromwich R, et al. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J. 2019;70:107–18.CrossRef
10.
go back to reference Kohli M, Geis R. Ethics, artificial intelligence, and radiology. J Am Coll Radiol. 2018;15:1317–9.CrossRef Kohli M, Geis R. Ethics, artificial intelligence, and radiology. J Am Coll Radiol. 2018;15:1317–9.CrossRef
12.
go back to reference Floridi L, Cowls J, Beltrametti M, et al. AI4People – an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Mind Mach. 2018;28:689–707.CrossRef Floridi L, Cowls J, Beltrametti M, et al. AI4People – an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Mind Mach. 2018;28:689–707.CrossRef
Metadata
Title
Ethical principles for the application of artificial intelligence (AI) in nuclear medicine
Authors
Geoff Currie
K Elizabeth Hawk
Eric M. Rohren
Publication date
01-04-2020
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 4/2020
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-020-04678-1

Other articles of this Issue 4/2020

European Journal of Nuclear Medicine and Molecular Imaging 4/2020 Go to the issue