Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

  • Kiduk Kim (Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Kyungjin Cho (Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Ryoungwoo Jang (Coreline Soft Co., Ltd.) ;
  • Sunggu Kyung (Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Soyoung Lee (Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Sungwon Ham (Healthcare Readiness Institute for Unified Korea, Korea University Ansan Hospital, Korea University College of Medicine) ;
  • Edward Choi (Korea Advanced Institute of Science and Technology) ;
  • Gil-Sun Hong (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Namkug Kim (Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center)
  • Received : 2023.08.28
  • Accepted : 2023.12.28
  • Published : 2024.03.01
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Abstract

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

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 HI22C1723).

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