Regulatory Needs for Radiation Protection Devices based upon Artificial Intelligence

State task or leave unregulated?

Authors

DOI:

https://doi.org/10.59667/sjoranm.v5i1.11

Keywords:

artificial intelligence, radiation protection, regulations, ensuring compliance

Abstract

Artificial intelligence (AI) is increasingly employed in radiation protection, encompassing both medical devices and software. These technologies are integrated with AI throughout their manufacturing and application processes. This article underscores the imperative for comprehensive regulation in the utilization of AI. Decisions regarding AI application should not solely rest with manufacturers, medical professionals, or patients. Instead, an overarching "neutral" authority must be engaged to regulate, review, and enforce adherence to established protocols. The authors contend that relying on "self-regulation" within the free market, absent clear guidelines, proves to be inadequately effective and leads to patient's radiation protection safety issues. 

Author Biographies

  • Gerd Nöldge, Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, University of Bern, University Hospital of Bern

    Founding Editor & Honorary Editor
    Switzerland & Germany
    Senior Consultant Radiology, University Hospitals Heidelberg & Bern

    Prof. Dr. med. Gerd Nöldge is a renowned German radiologist and pioneer in interventional radiology. As former Senior Consultant at Heidelberg University Hospital, he made groundbreaking contributions to gastrointestinal artery embolization, for which he received the inaugural Wilhelm Conrad Röntgen Prize in 1985. He played a key role in advancing kyphoplasty techniques for osteoporotic vertebral fractures, providing long-term evidence for the efficacy and stability of calcium phosphate cement in vertebral body augmentation.

    Prof. Nöldge has also significantly contributed to the clinical development and refinement of transjugular intrahepatic portosystemic shunt (TIPS) procedures, improving outcomes in patients with portal hypertension. His dedication to education and international collaboration has fostered enduring partnerships, including medical exchange programs with leading institutions in China.

    As Founding Editor of the Swiss Journal of Radiology and Nuclear Medicine, Prof. Nöldge played a decisive role in the establishment and early development of SJORANM. His appointment as Honorary Editor recognizes his founding contribution, his longstanding scientific commitment, and his lasting importance to the journal. 

    Former institutional affiliation: University Hospital Heidelberg – Diagnostic and Interventional Radiology
    Location: Heidelberg, Germany & Bern, Switzerland
    ROR: 013czdx64

  • Frank Mosler, Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital of Bern, University of Bern

    Board-certified Radiologist & Neuroradiologist; Senior Consultant, Inselspital, Bern University Hospital

    Dr. med. Frank Mosler is a board-certified radiologist and neuroradiologist with more than 30 years of clinical and academic experience across major European university centers. He completed his medical education and early specialty training in radiology and neurosurgery at Charité University Hospital in Berlin. He later specialized in interventional neuroradiology at Alfried Krupp Hospital in Essen, where he also held senior leadership responsibilities.

    His professional career has included academic medicine, diagnostic and interventional radiology, neuroradiology, healthcare management, quality assurance, and scientific publishing in Germany and Switzerland. Since 2017, he has been affiliated with the Department of Diagnostic, Interventional and Pediatric Radiology (DIPR) at Inselspital, Bern University Hospital.

    As Editor-in-Chief of the Swiss Journal of Radiology and Nuclear Medicine, Dr. Mosler leads the journal's editorial and scientific strategy and holds overall responsibility for editorial policy, publication standards, scientific integrity, and final editorial decision-making.

    Institutional affiliation: University Institute of Diagnostic, Interventional and Pediatric Radiology, Inselspital
    Location: Bern, Switzerland
    ROR: 01q9sj412

References

1. Barreiro-Ares A, Morales-Santiago A, Sendra-Portero F, Souto-Bayarri M. Impact of the rise of artificial intelligence in radiology: what do students think? International Journal of Environmental Research and Public Health 2023; 20(2): 1589. https://doi.org/10.3390/ijerph20021589 DOI: https://doi.org/10.3390/ijerph20021589

2. Bosbach WA, Senge JF, Nemeth B, et al. Ability of ChatGPT to generate competent radiology reports for distal radius fracture by use of RSNA template items and integrated AO classifier. Curr Probl Diagn Radiol. 2024;53(1):102–110. Published online 17 April 2023. https://doi.org/10.1067/j.cpradiol.2023.04.001 DOI: https://doi.org/10.1067/j.cpradiol.2023.04.001

3. Clark P, Kim J, Aphinyanaphongs Y. Marketing and US Food and Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical Devices: A Systematic Review. JAMA Netw Open 2023; 6(7) :e2321792. https://doi.org/10.1001/jamanetworkopen.2023.21792 DOI: https://doi.org/10.1001/jamanetworkopen.2023.21792

4. European Society of R. The role of radiologist in the changing world of healthcare: a White Paper of the European Society of Radiology (ESR). Insights Imaging 2022;13(1):100. https://doi.org/10.1186/s13244-022-01241-4 DOI: https://doi.org/10.1186/s13244-022-01241-4

5. 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(2): 436-440. https://doi.org/10.1148/radiol.2019191586 DOI: https://doi.org/10.1148/radiol.2019191586

6. Giansanti D. The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews. Healthcare: MDPI; 2022:1824. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601605/pdf/healthcare-10-01824.pdf DOI: https://doi.org/10.3390/healthcare10101824

7. Jacques T, Fournier L, Zins M, et al. Proposals for the use of artificial intelligence in emergency radiology. Diagnostic and Interventional Imaging 2021;102(2):63-68. https://doi.org/10.1016/j.diii.2020.11.003 DOI: https://doi.org/10.1016/j.diii.2020.11.003

8. Pesapane F, Suter MB, Codari M, Patella F, Volonté C, Sardanelli F. Regulatory issues for artificial intelligence in radiology. Precision medicine for investigators, practitioners and providers: Elsevier; 2020:533-543 (book section). DOI: https://doi.org/10.1016/B978-0-12-819178-1.00052-6

9. 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 into imaging 2018;9:745-753. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206380/pdf/13244_2018_Article_645.pdf DOI: https://doi.org/10.1007/s13244-018-0645-y

10. Recht MP, Dewey M, Dreyer K, et al. Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. European radiology 2020 ;30: 3576 -3584. https://doi.org/10.1007/s00330-020-06672-5 DOI: https://doi.org/10.1007/s00330-020-06672-5

11. Waymel Q, Badr S, Demondion X, Cotten A, Jacques T. Impact of the rise of artificial intelligence in radiology: what do radiologists think? Diagnostic and interventional imaging 2019;100(6):327-336. https://doi.org/10.1016/j.diii.2019.03.015 DOI: https://doi.org/10.1016/j.diii.2019.03.015

12. US Food & Drug Administration - Good Machine Learning Practice for Medical Device Development: Guiding Principles 2021. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles

13. Senge, J.F., McMurray, M.T. et al. ChatGPT may free time needed by the interventional radiologist for administration / documentation: A study on the RSNA PICC line reporting template 2023. medRXiv The Preprint Server for Health Sciences. https://doi.org/10.1101/2023.07.14.23292578 DOI: https://doi.org/10.1101/2023.07.14.23292578

14. US Food and Drug Administration. Statement from FDA Commissioner Scott Gottlieb, M.D. on steps toward a new, tailored review framework for artificial intelligence-based medical devices. 2 April 2019. https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md-steps-toward-new-tailored-review-framework-artificial

15. US Food and Drug Administration. CDRH Issues Guiding Principles for Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices. 24 October 2023. https://www.fda.gov/medical-devices/medical-devices-news-and-events/cdrh-issues-guiding-principles-predetermined-change-control-plans-machine-learning-enabled-medical

16. US Food and Drug Administration. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning-Enabled Device Software Functions; Draft Guidance for Industry and Food and Drug Administration Staff; Availability. Federal Register. 3 April 2023;88:19648–19650. https://www.govinfo.gov/content/pkg/FR-2023-04-03/pdf/2023-06786.pdf

17. Swiss Federal Council. Medical Devices Ordinance of 1 July 2020 (MedDO/MepV), SR 812.213. Version of 1 November 2023. Article 16(2)–(4). https://www.fedlex.admin.ch/eli/cc/2020/552/20231101/de

SJORANM article cover, Vol. 5 No. 1 (2024). Holographic symbolic illustration for article 18.

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2024-02-24

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Vol. 5 No. 1 (2024)

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Copyright (c) 2024 Stefanie Nicole Garni, Nando Mertineit, Gerd Nöldge, Keivan Daneshvar; Frank Mosler

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How to Cite

Garni, S. N., Mertineit, N., Nöldge, G., Daneshvar, K., & Mosler, F. (2024). Regulatory Needs for Radiation Protection Devices based upon Artificial Intelligence: State task or leave unregulated?. Swiss Journal of Radiology and Nuclear Medicine, 5(1), 5. https://doi.org/10.59667/sjoranm.v5i1.11

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