Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology

  • Nicole Kessa Wee (Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group) ;
  • Kim-Ann Git (Department of Diagnostic Radiology, Pantai Hospital) ;
  • Wen-Jeng Lee (Department of Diagnostic Radiology, National Taiwan University Hospital) ;
  • Gaurang Raval (Department of Diagnostic Radiology, Workhardt Hospitals Limited) ;
  • Aziz Pattokhov (Faculty of Medicine, Tashkent State Dental Institute) ;
  • Evelyn Lai Ming Ho (Department of Diagnostic Radiology, ParkCity Medical Centre) ;
  • Chamaree Chuapetcharasopon (Department of Diagnostic Radiology, MedPark Hospital) ;
  • Noriyuki Tomiyama (Department of Diagnostic and Interventional Radiology Suita, Osaka University Hospital) ;
  • Kwan Hoong Ng (Department of Biomedical Imaging and University of Malaya Research Imaging Centre, University of Malaya) ;
  • Cher Heng Tan (Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group)
  • Received : 2024.04.30
  • Accepted : 2024.05.14
  • Published : 2024.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

Keywords

Acknowledgement

We would like to thank the executive committee of the Asian-Oceanian Society of Radiology for approving this position statement.

References

  1. U.S. Food and Drug Administration. Artificial intelligence and machine learning (AI/ML)-enabled medical devices [accessed on March 31, 2024]. Available at: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
  2. Mun SK, Wong KH, Lo SB, Li Y, Bayarsaikhan S. Artificial intelligence for the future radiology diagnostic service. Front Mol Biosci 2021;7:614258
  3. Wu K, Wu E, Theodorou B, Liang W, Mack C, Glass L, et al. Characterizing the clinical adoption of medical AI devices through U.S. insurance claims. NEJM AI 2023;1:AIoa2300030
  4. Sung JJY, Savulescu J, Ngiam KY, An B, Ang TL, Yeoh KG, et al. Artificial intelligence for gastroenterology: Singapore artificial intelligence for gastroenterology working group position statement. J Gastroenterol Hepatol 2023;38:1669-1676
  5. Kim DW, Jang HY, Ko Y, Son JH, Kim PH, Kim SO, et al. Inconsistency in the use of the term "validation" in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging. PLoS One 2020;15:e0238908
  6. Habehh H, Gohel S. Machine learning in healthcare. Curr Genomics 2021;22:291-300
  7. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J 2019;6:94-98
  8. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018;15:20170387
  9. Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, et al. Deep learning in medical imaging and radiation therapy. Med Phys 2019;46:e1-e36
  10. Tripathi S, Gabriel K, Dheer S, Parajuli A, Augustin AI, Elahi A, et al. Understanding biases and disparities in radiology AI datasets: a review. J Am Coll Radiol 2023;20:836-841
  11. Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond. Inf Fusion 2022;77:29-52
  12. van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal 2022;79:102470
  13. GDPR.EU. What is GDPR, the EU's new data protection law? [accessed on June 11, 2024]. Available at: https://gdpr.eu/what-is-gdpr/?cn-reloaded=1
  14. Makkar A, Santosh KC. SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays. Int J Mach Learn Cybern 2023;14:2659-2670
  15. Konecny J, McMahan B, Ramage D. Federated optimization: distributed optimization beyond the datacenter. arXiv [Preprint]. 2015 [accessed on November 5, 2023]. Available at: https://doi.org/10.48550/arXiv.1511.03575
  16. Liu P, Xu X, Wang W. Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives. Cybersecurity 2022;5:4
  17. Dwork C, Roth A. The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 2014;9:211-407
  18. Carter RE, Anand V, Harmon Jr DM, Pellikka PA. Model drift: when it can be a sign of success and when it can be an occult problem. Intell Based Med 2022;6:100058
  19. Futoma J, Simons M, Panch T, Doshi-Velez F, Celi LA. The myth of generalisability in clinical research and machine learning in health care. Lancet Digit Health 2020;2:e489-e492
  20. Ministry of Health. Artificial intelligence in healthcare guidelines (AIHGIe) [accessed on October 26, 2023]. Available at: https://www.moh.gov.sg/docs/librariesprovider5/eguides/1-0-artificial-in-healthcare-guidelines-(aihgle)_publishedoct21.pdf
  21. European Commission. White paper on artificial intelligence: a European approach to excellence and trust [accessed on October 26, 2023]. Available at: https://commission.europa.eu/document/d2ec4039-c5be-423a-81ef-b9e44e79825b_en
  22. Juluru K, Shih HH, Keshava Murthy KN, Elnajjar P, El-Rowmeim A, Roth C, et al. Integrating Al algorithms into the clinical workflow. Radiol Artif Intell 2021;3:e210013
  23. Blezek DJ, Olson-Williams L, Missert A, Korfiatis P. AI integration in the clinical workflow. J Digit Imaging 2021;34:1435-1446
  24. World Health Organization. Ethics and governance of artificial intelligence for health [accessed on November 5, 2023]. Available at: https://www.who.int/publications/i/item/9789240029200
  25. Geis JR, Brady A, Wu CC, Spencer J, Ranschaert E, Jaremko JL, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging 2019;10:101
  26. Dawson D, Schleiger E, Horton J, McLaughlin J, Robinson C, Quezada G, et al. Artificial intelligence: Australia's ethics framework [accessed on October 26, 2023]. Available at: https://www.csiro.au/-/media/D61/Reports/Artificial-Intelligence-ethics-framework.pdf
  27. CIFAR. CIFAR and the French National Centre for Scientific Research (CNRS) establish CAD $1M research agreement [accessed on November 5, 2023]. Available at: https://cifar.ca/cifarnews/2021/05/12/cifar-and-the-french-national-centre-for-scientific-research-cnrs-establish-cad-1m-research-agreement
  28. European Commission. France AI strategy report [accessed on November 5, 2023]. Available at: https://ai-watch.ec.europa.eu/countries/france/france-ai-strategy-report_en
  29. GOV.UK. Centre for data ethics and innovation [accessed on November 5, 2023]. Available at: https://www.gov.uk/government/organisations/centre-for-data-ethics-and-innovation
  30. European Commission. Ethics guidelines for trustworthy AI [accessed on October 26, 2023]. Available at: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
  31. Saw SN, Ng KH. Current challenges of implementing artificial intelligence in medical imaging. Phys Med 2022;100:12-17
  32. U.S. Food and Drug Administration. Artificial intelligence & medical products: how CBER, CDER, CDRH, and OCP are working together [accessed on April 1, 2024]. Available at: https://www.fda.gov/media/177030/download
  33. European Coordination Committee of the Radiological, Electromedical and Healthcare IT Industry (COCIR). Artificial intelligence in EU medical device legislation [accessed on October 26, 2023]. Available at: https://www.cocir.org/fileadmin/Position_Papers_2020/COCIR_Analysis_on_AI_in_medical_Device_Legislation_-_Sept._2020_-_Final_2.pdf
  34. Maliha G, Gerke S, Cohen IG, Parikh RB. Artificial intelligence and liability in medicine: balancing safety and innovation. Milbank Q 2021;99:629-647
  35. Justia US Law. Ross v. Jacobs [accessed on June 11, 2024]. Available at: https://law.justia.com/cases/oklahoma/court-of-appeals-civil/1984/9765.html
  36. Price WN 2nd, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA 2019;322:1765-1766
  37. U.S. Food and Drug Administration. Predetermined change control plans for machine learning-enabled medical devices: guiding principles [accessed on April 1, 2024]. Available at: https://www.fda.gov/medical-devices/software-medical-device-samd/predetermined-change-control-plans-machine-learning-enabled-medical-devices-guiding-principles
  38. Nguyen NH, Nguyen HQ, Nguyen NT, Nguyen TV, Pham HH, Nguyen TN. Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings. Front Digit Health 2022;4:890759
  39. Qure.ai. Scaling up TB screening with AI: deploying automated X-ray screening in remote regions [accessed on October 26, 2023]. Available at: https://www.qure.ai/blog/scaling-up-tb-screening-with-ai-deploying-automated-x-ray-screening-in-remote-regions
  40. Leibig C, Brehmer M, Bunk S, Byng D, Pinker K, Umutlu L. Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis. Lancet Digit Health 2022;4:e507-e519
  41. Hallinan JTPD, Zhu L, Yang K, Makmur A, Algazwi DAR, Thian YL, et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI. Radiology 2021;300:130-138
  42. Lincoln CM, Chatterjee R, Willis MH. Augmented radiology: looking over the horizon. Radiol Artif Intell 2019;1:e180039
  43. American College of Radiology. ACR AI-Lab main page [accessed on October 26, 2023]. Available at: https://ailab.acr.org/Account/Home.
  44. Ardestani A, Li MD, Chea P, Wortman JR, Medina A, Kalpathy-Cramer J, et al. External COVID-19 deep learning model validation on ACR AI-LAB: it's a brave new world. J Am Coll Radiol 2022;19:891-900