AI-Generated Content Enhanced Computer-Aided Diagnosis Model for Thyroid Nodules: A ChatGPT-Style Assistant
<|reference_start|>AI-Generated Content Enhanced Computer-Aided Diagnosis Model for Thyroid Nodules: A ChatGPT-Style Assistant: 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.<|reference_end|>
@article{yao2024ai-generated,
    title={AI-Generated Content Enhanced Computer-Aided Diagnosis Model for Thyroid
  Nodules: A ChatGPT-Style Assistant},
    author={Jincao Yao (1 and 2 and 3 and 4 and 5 and 6), Yunpeng Wang (7), Zhikai
  Lei (8), Kai Wang (9), Xiaoxian Li (10) Jianhua Zhou (10), Xiang Hao (7),
  Jiafei Shen (1 and 2), Zhenping Wang (9), Rongrong Ru (11), Yaqing Chen (11),
  Yahan Zhou (6), Chen Chen (1 and 2), Yanming Zhang (12 and 13), Ping Liang
  (14), Dong Xu (1 and 2 and 3 and 4 and 5 and 6) ((1) Department of Radiology,
  Zhejiang Cancer Hospital, Hangzhou, 310022, China (2) Hangzhou Institute of
  Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310000, China,(3) Key
  Laboratory of Head and Neck Cancer Translational Research of Zhejiang
  Province, Hangzhou, 310022, China,(4) Zhejiang Provincial Research Center for
  Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310000,
  China, (5) Wenling Medical Big Data and Artificial Intelligence Research
  Institute, 24th Floor, Machang Road, Taizhou, 310061, China,(6) Taizhou Key
  Laboratory of Minimally Invasive Interventional Therapy and Artificial
  Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer
  Hospital), Taizhou, 317502, China,(7) College of Optical Science and
  Engineering, Zhejiang University, No.38 of Zheda Road, Hangzhou, Zhejiang
  Province, China,(8) Zhejiang Provincial Hospital of Chinese Medicine, 54
  Youdian Road, Hangzhou, 310003, China,(9) Department of Ultrasound, The
  Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, 322100,
  China,(10) Department of Ultrasound, Sun Yat sen University Cancer Center,
  State Key Laboratory of Oncology in South China, Collaborative Innovation
  Center for Cancer Medicine, Guangzhou, 510060, China, (11) Affiliated
  Xiaoshan Hospital, Hangzhou Normal University, No.728 North Yucai Road,
  Hangzhou, 311202, China,(12) Zhejiang Provincial People's Hospital Affiliated
  People's Hospital, Hangzhou Medical College, Hangzhou, 314408, China,(13) Key
  Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou,
  314408, China,(14) Department of Ultrasound, Chinese PLA General Hospital,
  Chinese PLA Medical School, Beijing, 100853, China)},
    journal={arXiv preprint arXiv:2402.02401},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02401},
    primaryClass={cs.CV cs.AI}
}