Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.

Journal: Korean journal of radiology
Published Date: Mar 1, 2024

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.

Authors

  • Kiduk Kim
    Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
  • Kyungjin Cho
    Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Ryoungwoo Jang
    Department of Biomedical Engineering, Asan Institute of Life Science, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.
  • Sunggu Kyung
    Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea.
  • Soyoung Lee
    Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul, 05505, South Korea.
  • Sungwon Ham
    Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. (S.H.).
  • Edward Choi
    College of Computing Georgia Institute of Technology Atlanta, GA, USA.
  • Gil-Sun Hong
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine & Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu Seoul 05505, Republic of Korea. Electronic address: hgs2013@gmail.com.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

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