Zero-shot classification of TNM staging for Japanese radiology report using ChatGPT at RR-TNM subtask of NTCIR-17 MedNLP-SC

We describe our submission to the RR-TNM subtask of the NTCIR-17 MedNLP-SC shared task. In the RR-TNM subtask, we developed our system for automatic extraction and classification of the TNM staging from Japanese radiology reports of lung cancers. In our system, zero-shot classification and prompt engineering were performed using ChatGPT and LangChain, respectively. According to the accuracies calculated by the organizers of the RR-TNM subtask, the accuracies of N and M factors in the TNM staging were higher in our submission than in the other submissions. These results indicate that our system with ChatGPT and LangChain may be promising.