ArticleJMIR medical informatics2025
Large Language Models as a Consulting Hotline for Patients With Breast Cancer and Specialists in China: Cross-Sectional Questionnaire Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Evaluating Large Language Models as Medical Consultation Tools for Double Eyelid Surgery: A Cross-Language Study in English and Chinese.Aesthetic plastic surgery · 2026Article
- Large language models for breast cancer treatment planning: a blinded real-world evaluation of DeepSeek, ChatGPT, and oncologist recommendations.Frontiers in digital health · 2026Article
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Authors and funding
7 authors.
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Abstract
Background: The 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. Objective: This 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). Methods: The 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. Results: Overall, 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<.001). However, the responses from all LLMs are relatively generalized, leading to lower accuracy and practicality for the expert questionnaire compared to the patient questionnaire. Additionally, there were issues such as the lack of supporting evidence and potential ethical risks in the responses of LLMs. Conclusions: Currently, 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.
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