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.

通过语义级人机交互的方式，基于 ChatGPT 架构设计出了一种名为 ThyGPT 的人工智能增强型计算机辅助诊断（AIGC-CAD）模型，可以协助放射科医生评估甲状腺结节的风险。使用由浙江癌症医院收集的 19,165 个甲状腺结节超声案例的数据集进行模型的训练和验证。经过训练后，ThyGPT 能够自动评估甲状腺结节并通过人机交互与医生进行有效沟通。实证结果表明，辅助 ThyGPT 的放射科医生在诊断能力上显著优于使用传统方法的同行，并且优于孤立模型的性能。这些发现表明，像 ThyGPT 这样的 AIGC-CAD 系统有望从根本上改变放射科医生的诊断工作流程。