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Semin intervent Radiol 2023; 40(03): 323-326
DOI: 10.1055/s-0043-1769905
Ethics Corner

Ethical Considerations for Artificial Intelligence in Interventional Radiology: Balancing Innovation and Patient Care

Helena D. Rockwell
1   School of Medicine, University of California, San Diego, La Jolla, California
,
Eric D. Cyphers
2   Department of Bioethics, Columbia University, New York, New York
3   Philadelphia College of Osteopathic Medicine, Philadelphia, Pennsylvania
,
Mina S. Makary
4   Division of Interventional Radiology, Department of Radiology, The Ohio State University, Columbus, Ohio
,
Eric J. Keller
5   Division of Interventional Radiology, Department of Radiology, Stanford University Medical Center, Stanford, California
› Author Affiliations Funding None.
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Artificial intelligence (AI) encompasses computational algorithms that, partially or completely, autonomously perform beneficial tasks usually considered representative of human intelligence.[1] This revolutionary technology has the potential to shape the scope of healthcare in incredible ways. From data-driven treatment recommendations, real-time intraprocedural support, predicting outcomes, and more, there are vast possibilities for implementing AI in interventional radiology (IR) to help maximize patient care.[2] [3] [4] [5] While there exists much enthusiasm for integrating this cutting-edge technology in IR, there are many ethical issues to consider in its use, such as questions about data ownership and distribution, culpability in the setting of AI-associated adverse events, and amplification of inequities and bias. This article explores some of these challenges and suggests a framework for navigating them.



Publication History

Article published online:
20 July 2023

© 2023. Thieme. All rights reserved.

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  • References

  • 1 Russell S, Norvig P. Artificial Intelligence: A Modern Approach. 2nd ed.. Upper Saddle River, NJ: Prentice-Hall; 2003
    Google Scholar
  • 2 Abajian A, Murali N, Savic LJ. et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning—an artificial intelligence concept. J Vasc Interv Radiol 2018; 29 (06) 850-857.e1
    CrossrefPubMedGoogle Scholar
  • 3 Gurgitano M, Angileri SA, Rodà GM. et al. Interventional radiology ex-machina: impact of artificial intelligence on practice. Radiol Med (Torino) 2021; 126 (07) 998-1006
    CrossrefPubMedGoogle Scholar
  • 4 von Ende E, Ryan S, Crain MA, Makary MS. Artificial intelligence, augmented reality, and virtual reality advances and applications in interventional radiology. Diagnostics (Basel) 2023; 13 (05) 892
    CrossrefPubMedGoogle Scholar
  • 5 Malpani R, Petty CW, Bhatt N, Staib LH, Chapiro J. Use of artificial intelligence in nononcologic interventional radiology: current state and future directions. Dig Dis Interv 2021; 5 (04) 331-337
    Article in Thieme ConnectPubMedGoogle Scholar
  • 6 Mearian L. Yes, Google's using your healthcare data – and it's not alone. Computer World. Accessed March 30, 2023 at: https://www.computerworld.com/article/3453818/yes-googles-using-your-healthcare-data-and-its-not-alone.html. Published November 15, 2019
    PubMedGoogle Scholar
  • 7 Austin PL. The real reason Google is buying Fitbit. Time. Accessed March 30, 2023 at: https://time.com/5717726/google-fitbit/. Published November 4, 2019
    PubMed
  • 8 Larson DB, Magnus DC, Lungren MP, Shah NH, Langlotz CP. Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework. Radiology 2020; 295 (03) 675-682
    CrossrefPubMedGoogle Scholar
  • 9 Fernandez-Quilez A. Deep learning in radiology: ethics of data and on the value of algorithm transparency, interpretability and explainability. AI Ethics. 2022; 3 (01) 257-265
    CrossrefPubMedGoogle Scholar
  • 10 Brady AP, Neri E. Artificial intelligence in radiology—ethical considerations. Diagnostics (Basel) 2020; 10 (04) 231
    CrossrefPubMedGoogle Scholar
  • 11 Faden RR, Kass NE, Goodman SN, Pronovost P, Tunis S, Beauchamp TL. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics. Hastings Cent Rep 2013; 43 (Spec No, s1): S16-S27
    CrossrefPubMedGoogle Scholar
  • 12 Anom BY. Ethics of big data and artificial intelligence in medicine. Ethics Med Public Health 2020; 15: 100568
    CrossrefPubMedGoogle Scholar
  • 13 Aggarwal R, Farag S, Martin G, Ashrafian H, Darzi A. Patient perceptions on data sharing and applying artificial intelligence to health care data: cross-sectional survey. J Med Internet Res 2021; 23 (08) e26162
    CrossrefPubMedGoogle Scholar
  • 14 Kruse CS, Frederick B, Jacobson T, Monticone DK. Cybersecurity in healthcare: a systematic review of modern threats and trends. Technol Health Care 2017; 25 (01) 1-10
    CrossrefPubMedGoogle Scholar
  • 15 Geis JR, Brady AP, Wu CC. et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology 2019; 293 (02) 436-440
    CrossrefPubMedGoogle Scholar
  • 16 Bhuyan SS, Kabir UY, Escareno JM. et al. Transforming healthcare cybersecurity from reactive to proactive: current status and future recommendations. J Med Syst 2020; 44 (05) 98
    CrossrefPubMedGoogle Scholar
  • 17 Levinson W. Disclosing medical errors to patients: a challenge for health care professionals and institutions. Patient Educ Couns 2009; 76 (03) 296-299
    CrossrefPubMedGoogle Scholar
  • 18 Keller EJ. Reflect and remember: the ethics of complications in interventional radiology. Semin Intervent Radiol 2019; 36 (02) 104-107
    Article in Thieme ConnectPubMedGoogle Scholar
  • 19 O'Connor E, Coates HM, Yardley IE, Wu AW. Disclosure of patient safety incidents: a comprehensive review. Int J Qual Health Care 2010; 22 (05) 371-379
    CrossrefPubMedGoogle Scholar
  • 20 Phillips-Bute PD. Transparency and disclosure of medical errors: It's the right thing to do, so why the reluctance?. Campbell Law Rev 2013; 35 (03) 333
    PubMedGoogle Scholar
  • 21 Keller EJ, Vogelzang RL. Providing context: medical device litigation and inferior vena cava filters. Semin Intervent Radiol 2016; 33 (02) 132-136
    Article in Thieme ConnectPubMedGoogle Scholar
  • 22 Morley J, Machado CCV, Burr C. et al. The ethics of AI in health care: a mapping review. Soc Sci Med 2020; 260: 113172
    CrossrefPubMedGoogle Scholar
  • 23 Pesapane F, Volonté C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging 2018; 9 (05) 745-753
    CrossrefPubMedGoogle Scholar
  • 24 Zhang Z, Citardi D, Wang D, Genc Y, Shan J, Fan X. Patients' perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data. Health Informatics J 2021; 27 (02) 14 604582211011215
    CrossrefPubMedGoogle Scholar
  • 25 Verghese A, Shah NH, Harrington RA. What this computer needs is a physician: humanism and artificial intelligence. JAMA 2018; 319 (01) 19-20
    CrossrefPubMedGoogle Scholar
  • 26 Tejani A. Understanding bias in AI. Understanding Bias in AI. Accessed March 1, 2023 at: https://www.acr.org/Practice-Management-Quality-Informatics/ACR-Bulletin/Articles/February-2023/Understanding-Bias-in-AI. Published January 23, 2023
    PubMed
  • 27 Trivedi PS, Guerra B, Kumar V. et al. Healthcare disparities in interventional radiology. J Vasc Interv Radiol 2022; 33 (12) 1459-1467.e1
    CrossrefPubMedGoogle Scholar