Large-Scale assessment of ChatGPT's performance in benign and malignant bone tumors imaging report diagnosis and its potential for clinical applications.

Objective: This study was designed to delve into the complexities involved in diagnosing of benign and malignant bone tumors and to assess the potential of AI technologies like ChatGPT in improving diagnostic accuracy and efficiency. The study also explores the few-shot learning as a method to optimize ChatGPT's performance in specialized medical domains such as benign and malignant bone tumors diagnosis. Methods: A total of 1366 benign and malignant bone tumors-related imaging reports were collected and diagnosed by 25 experienced physicians. The gold standard of diagnosis was established by combining clinical, imaging and pathological principles.These reports were then input into the ChatGPT model which underwent a few-shot learning method to generate diagnostic results. The diagnostic results of the physicians and the AI model were compared to evaluate the performance of ChatGPT. An experiment was conducted to assess the influence of different radiologist's reporting styles on the model's diagnostic performance. Furthermore, in-depth analysis of misdiagnosed cases was carried out, categorizing diagnostic errors and exploring possible causes. Results: The diagnostic results generated by ChatGPT showed an accuracy of 0.73, sensitivity of 0.95, and specificity of 0.58. After few-shot learning, ChatGPT demonstrated significant improvement, achieving an accuracy of 0.87, sensitivity of 0.99, and specificity of 0.73, bringing it much closer to the level of physician diagnostics. In an experiment analyzing the influence of the radiologist's reporting style, the model demonstrated higher sensitivity when interpreting reports written by high-level radiologists. In 56 benign cases, ChatGPT misdiagnosed them as malignant. Among these, 35 benign lesions- fibrous dysplasia and osteofibrous dysplasia- were incorrectly identified as metastatic tumors or osteosarcomas; 8 cases of myositis ossificans were wrongly diagnosed as extraosseous osteosarcoma. 7 cases of giant cell tumor of bone at the end of long bone were misdiagnosed as osteosarcoma by intermediate doctors. Chondroblastoma was misdiagnosed as malignant tumor in 6 cases -2 osteosarcoma and 4 chondrosarcoma-In this study, 23 osteosarcoma cases were misdiagnosed by ChatGPT as osteomyelitis; Chondrosarcoma was misdiagnosed as fibrous dysplasia or aneurysmal bone cyst in 8 cases. Four cases of spinal chordoma were misdiagnosed as spinal tuberculosis. Conclusion: Our findings highlight the potential of ChatGPT in the diagnosis of benign and malignant bone tumors, offering advantages like enhanced efficiency and a reduction in missed diagnoses. However, the necessity of collaborative interactions between physicians and ChatGPT in practical settings was underscored. With an examination into AI's capacity in benign and malignant bone tumors diagnosis, this study lays the groundwork for future AI advancements in medicine. Additionally, the benefits of few-shot learning in fine-tuning ChatGPT applications in specialized fields were also demonstrated.