Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning

  • Gil-Sun Hong (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Miso Jang (Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Sunggu Kyung (Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Kyungjin Cho (Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Jiheon Jeong (Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Grace Yoojin Lee (Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Keewon Shin (Laboratory for Biosignal Analysis and Perioperative Outcome Research, Biomedical Engineering Center, Asan Institute of Lifesciences, Asan Medical Center) ;
  • Ki Duk Kim (Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Seung Min Ryu (Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Joon Beom Seo (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Sang Min Lee (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Namkug Kim (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • Received : 2023.04.27
  • Accepted : 2023.07.30
  • Published : 2023.11.01
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Abstract

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

Keywords

Acknowledgement

This research was supported by grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI21C1148 and HI22C172300).

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