Generative artificial intelligence in medical imaging: Current landscape, challenges, and future directions

Abstract

The clinical significance, digital attributes, and underlying high-dimensional information in medical images make them a key area for the artificial intelligence (AI) revolution in health care. Generative AIs (GAIs) provide unprecedented abilities in synthesizing diverse and accurate simulated medical images for AI model training as well as personalized disease management. However, several hurdles must be overcome prior to clinical implementation, such as biases introduced during training in synthesized images and the risk of medical and research falsification. This review outlines the current landscape of medical image synthesis through GAIs, with a specific focus on the variety of medical images to be synthesized, various real-world issues to be solved, and the evaluation of the quality and utility of the synthesized images. We finally summarize the key challenges, propose potential solutions, and highlight promising directions for future research, with the aim of providing guidance for upcoming research.