  An artificial intelligence-generated content-enhanced computer-aided
diagnosis (AIGC-CAD) model, designated as ThyGPT, has been developed. This
model, inspired by the architecture of ChatGPT, could assist radiologists in
assessing the risk of thyroid nodules through semantic-level human-machine
interaction. A dataset comprising 19,165 thyroid nodule ultrasound cases from
Zhejiang Cancer Hospital was assembled to facilitate the training and
validation of the model. After training, ThyGPT could automatically evaluate
thyroid nodule and engage in effective communication with physicians through
human-computer interaction. The performance of ThyGPT was rigorously quantified
using established metrics such as the receiver operating characteristic (ROC)
curve, area under the curve (AUC), sensitivity, and specificity. The empirical
findings revealed that radiologists, when supplemented with ThyGPT, markedly
surpassed the diagnostic acumen of their peers utilizing traditional methods as
well as the performance of the model in isolation. These findings suggest that
AIGC-CAD systems, exemplified by ThyGPT, hold the promise to fundamentally
transform the diagnostic workflows of radiologists in forthcoming years.
