  Large language models (LLMs) have recently demonstrated their potential in
clinical applications, providing valuable medical knowledge and advice. For
example, a large dialog LLM like ChatGPT has successfully passed part of the US
medical licensing exam. However, LLMs currently have difficulty processing
images, making it challenging to interpret information from medical images,
which are rich in information that supports clinical decisions. On the other
hand, computer-aided diagnosis (CAD) networks for medical images have seen
significant success in the medical field by using advanced deep-learning
algorithms to support clinical decision-making. This paper presents a method
for integrating LLMs into medical-image CAD networks. The proposed framework
uses LLMs to enhance the output of multiple CAD networks, such as diagnosis
networks, lesion segmentation networks, and report generation networks, by
summarizing and reorganizing the information presented in natural language text
format. The goal is to merge the strengths of LLMs' medical domain knowledge
and logical reasoning with the vision understanding capability of existing
medical-image CAD models to create a more user-friendly and understandable
system for patients compared to conventional CAD systems. In the future, LLM's
medical knowledge can be also used to improve the performance of vision-based
medical-image CAD models.
