Large Language Models as a Consulting Hotline for Patients With Breast Cancer and Specialists in China: Cross-Sectional Questionnaire Study - Hui Liu; Jialun Peng; Lu Li; Ao Deng; XiangXin Huang; Guobing Yin; Haojun Luo (JMIR Medical Informatics)
Abstract BackgroundThe disease burden of breast cancer is increasing in China. Guiding people to obtain accurate information on breast cancer and improving the public’s health literacy are crucial for the early detection and timely treatment of breast cancer. Large language model (LLM) is a currently popular source of health information. However, the accuracy and practicality of the breast cancer–related information provided by LLMs have not yet been evaluated. ObjectiveThis study aims to evaluate and compare the accuracy, practicality, and generalization-specificity of responses to breast cancer–related questions from two LLMs, ChatGPT and ERNIE Bot (EB). MethodsThe questions asked to the LLMs consisted of a patient questionnaire and an expert questionnaire, each containing 15 questions. ChatGPT was queried in both Chinese and English, recorded as ChatGPT-Chinese (ChatGPT-C) and ChatGPT-English (ChatGPT-E) respectively, while EB was queried in Chinese. The accuracy, practicality, and generalization-specificity of each inquiry’s responses were rated by a breast cancer multidisciplinary treatment team using Likert scales. ResultsOverall, for both the patient and expert questionnaire, the accuracy and practicality of responses from ChatGPT-E were significantly higher than those from ChatGPT-C and EB (all Ps ConclusionsCurrently, compared to other LLMs, ChatGPT-E has demonstrated greater potential for application in educating Chinese patients with breast cancer, and may serve as an effective tool for them to obtain health information. However, for breast cancer specialists, these LLMs are not yet suitable for assisting in clinical diagnosis or treatment activities. Additionally, data security, ethical, and legal risks associated with using LLMs in clinical practice cannot be ignored. In the future, further research is needed to determine the true efficacy of LLMs in clinical scenarios related to breast cancer in China.