Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing-Reference-Cited by-同舟云学术

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing

Published:2024-01-01 Issue:1 Volume:1 Page:
ISSN:2976-8705
Container-title:BJR|Artificial Intelligence
language:en
Short-container-title:

Author:

Mahmood Usman¹^ORCID,Shukla-Dave Amita¹²,Chan Heang-Ping³^ORCID,Drukker Karen⁴^ORCID,Samala Ravi K⁵^ORCID,Chen Quan⁶,Vergara Daniel⁷^ORCID,Greenspan Hayit⁸,Petrick Nicholas⁵^ORCID,Sahiner Berkman⁵,Huo Zhimin⁹,Summers Ronald M¹⁰^ORCID,Cha Kenny H⁵,Tourassi Georgia¹¹,Deserno Thomas M¹²^ORCID,Grizzard Kevin T¹³,Näppi Janne J¹⁴,Yoshida Hiroyuki¹⁴,Regge Daniele¹⁵¹⁶,Mazurchuk Richard¹⁷,Suzuki Kenji¹⁸,Morra Lia¹⁹,Huisman Henkjan²⁰,Armato Samuel G⁴,Hadjiiski Lubomir³

Affiliation:

1. Department of Medical Physics, Memorial Sloan-Kettering Cancer Center , New York, NY, 10065, United States

2. Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, NY, 10065, United States

3. Department of Radiology, University of Michigan , Ann Arbor, MI, 48109, United States

4. Department of Radiology, University of Chicago , Chicago, IL, 60637, United States

5. Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration , Silver Spring, MD, 20993, United States

6. Department of Radiation Oncology, Mayo Clinic Arizona , Phoenix, AZ, 85054, United States

7. Department of Radiology, University of Washington , Seattle, WA, 98195, United States

8. Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mt Sinai , New York, NY, 10029, United States

9. Tencent America , Palo Alto, CA, 94306, United States

10. Radiology and Imaging Sciences, National Institutes of Health Clinical Center , Bethesda, MD, 20892, United States

11. Computing and Computational Sciences Directorate, Oak Ridge National Laboratory , Oak Ridge, TN, 37830, United States

12. Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School , Braunschweig, Niedersachsen, 38106, Germany

13. Department of Radiology and Biomedical Imaging, Yale University School of Medicine , New Haven, CT, 06510, United States

14. 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Boston, MA, 02114, United States

15. Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS , Candiolo, 10060, Italy

16. Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa , Pisa, 56126, Italy

17. Division of Cancer Prevention, National Cancer Institute, National Institutes of Health , Bethesda, MD, 20892, United States

18. Institute of Innovative Research, Tokyo Institute of Technology , Midori-ku, Yokohama, Kanagawa, 226-8503, Japan

19. Department of Control and Computer Engineering, Politecnico di Torino , Torino, Piemonte, 10129, Italy

20. Radboud Institute for Health Sciences, Radboud University Medical Center , Nijmegen, Gelderland, 6525 GA, Netherlands

Abstract

Abstract The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.

Funder

MIDRC

The Medical Imaging and Data Resource Center

National Institute of Biomedical Imaging and Bioengineering

National Institutes of Health

National Institutes of Health Clinical Center

Massachusetts General Hospital Executive Committee on Research

Publisher

Oxford University Press (OUP)

Link

https://academic.oup.com/bjrai/article-pdf/1/1/ubae003/56776982/ubae003.pdf

Reference19 articles.

1. Imaging artificial intelligence: a framework for radiologists to address health equity, from the AJR special series on DEI;Davis;AJR,2023

2. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement;Geis;Radiology,2019

3. Mapping the landscape of care providers’ quality assurance approaches for AI in diagnostic imaging;Lundström;J Digit Imaging,2023

4. Is artificial intelligence (AI) a pipe dream? Why legal issues present significant hurdles to AI autonomy;Mezrich;AJR Am J Roentgenol,2022

5. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States;Pesapane;Insights Imaging,2018