Penulis: Peng Wang , Wenpeng Lu , Chunlin Lu, Ruoxi Zhou, Min Li, and Libo Qin
Abstrak:
The advent of Large Language Models (LLMs) has sparked considerable interest in the medical
image domain, as they can generalize to multiple tasks and offer outstanding performance. While LLMs
achieve promising results, there is currently a lack of a comprehensive summary of medical images, making it
challenging for researchers to understand the progress within this domain. To fill this gap, we make the first
attempt to present a comprehensive survey for LLM on medical images. In addition, to better summarize the
current progress comprehensively, we further introduce a novel x-stage tuning paradigm for summarization,
including zero-stage tuning, one-stage tuning, and multi-stage tuning, offering a unified perspective on LLMs for
medical images. Finally, we discuss challenges and future directions in this domain, aiming to spur more
breakthroughs in the future. We hope this work can pave the way for the broad application of LLMs in medical
images and provide a valuable resource for this domain.