Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals
- Author:
Kiduk KIM
1
;
Kyungjin CHO
;
Ryoungwoo JANG
;
Sunggu KYUNG
;
Soyoung LEE
;
Sungwon HAM
;
Edward CHOI
;
Gil-Sun HONG
;
Namkug KIM
Author Information
- Publication Type:Review Article
- From:Korean Journal of Radiology 2024;25(3):224-242
- CountryRepublic of Korea
- Language:English
- 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.
