%0 Journal Article
%@ 2369-1999
%I JMIR Publications
%V 11
%N 
%P e66633
%T Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT
%A Chow,James C L
%A Li,Kay
%+ Department of Medical Physics, Princess Margaret Cancer Centre, University Health Network, Rm 7-606, 7/F, 700 University Ave, Toronto, ON, M5G 1X6, Canada, 1 416 946 4501, james.chow@uhn.ca
%K artificial intelligence
%K AI
%K AI in medical education
%K radiotherapy chatbot
%K large language models
%K LLMs
%K medical chatbots
%K health care AI
%K ethical AI in health care
%K personalized learning
%K natural language processing
%K NLP
%K radiotherapy education
%K AI-driven learning tools
%D 2025
%7 18.2.2025
%9 Viewpoint
%J JMIR Cancer
%G English
%X This Viewpoint proposes a robust framework for developing a medical chatbot dedicated to radiotherapy education, emphasizing accuracy, reliability, privacy, ethics, and future innovations. By analyzing existing research, the framework evaluates chatbot performance and identifies challenges such as content accuracy, bias, and system integration. The findings highlight opportunities for advancements in natural language processing, personalized learning, and immersive technologies. When designed with a focus on ethical standards and reliability, large language model–based chatbots could significantly impact radiotherapy education and health care delivery, positioning them as valuable tools for future developments in medical education globally.
%R 10.2196/66633
%U https://cancer.jmir.org/2025/1/e66633
%U https://doi.org/10.2196/66633