Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology

  • Eui Jin Hwang (Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine) ;
  • Ji Eun Park (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Kyoung Doo Song (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Dong Hyun Yang (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Kyung Won Kim (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • June-Goo Lee (Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine) ;
  • Jung Hyun Yoon (Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine) ;
  • Kyunghwa Han (Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine) ;
  • Dong Hyun Kim (Department of Radiology, Seoul Metropolitan Government–Seoul National University Boramae Medical Center, Seoul National University College of Medicine) ;
  • Hwiyoung Kim (Department of Biomedical Systems Informatics, Yonsei University College of Medicine) ;
  • Chang Min Park (Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine) ;
  • Radiology Imaging Network of Korea for Clinical Research (RINK-CR) (Radiology Imaging Network of Korea for Clinical Research (RINK-CR))
  • Received : 2023.12.15
  • Accepted : 2024.04.26
  • Published : 2024.07.01
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Abstract

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

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