Developing Effective Frameworks for LLM-Based Medical Chatbots: Insights from Radiotherapy Education with ChatGPT
ABSTRACT
The integration of AI, particularly large language models (LLMs), into medical education and healthcare has the potential to revolutionize these fields by providing innovative tools for learning and patient care. In radiotherapy education, AI-driven chatbots offer promising ways to enhance understanding of complex treatment protocols. This review aims to propose a resilient framework for developing a medical chatbot dedicated to radiotherapy education, emphasizing key factors such as accuracy, reliability, privacy, ethics, and potential future innovations. By analyzing existing research, the review explores the development process, evaluates chatbot performance, and identifies challenges such as content accuracy, bias, and system integration, while highlighting opportunities for advancements in natural language processing, personalized learning, and immersive technologies. The findings suggest that when designed with a focus on ethical standards and reliability, LLM-based chatbots could significantly impact radiotherapy education and healthcare delivery, positioning them as valuable tools for future developments in medical education globally.
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