Developing Effective Frameworks for Large Language Model-Based Medical Chatbots: Insights From Radiotherapy Education...

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Radiotherapy

Background:

• Radiotherapy education requires accurate and reliable tools.
• Existing educational methods may not fully leverage technological advancements.
• The potential of AI, specifically large language models (LLMs), in medical education is growing.

Purpose of the Study:

• To propose a framework for developing medical chatbots for radiotherapy education.
• To emphasize key development aspects: accuracy, reliability, privacy, ethics, and innovation.
• To evaluate current chatbot performance and identify challenges in this domain.

Main Methods:

• Analysis of existing research on medical chatbots and radiotherapy education.
• Evaluation of chatbot performance metrics.
• Identification of challenges including content accuracy, bias, and system integration.

Main Results:

• A robust framework for chatbot development has been outlined.
• Key challenges in implementing AI for radiotherapy education were identified.
• Opportunities for advancements in NLP, personalized learning, and immersive technologies were highlighted.

Conclusions:

• LLM-based chatbots, when ethically designed and reliable, can significantly impact radiotherapy education.
• These chatbots offer valuable tools for enhancing healthcare delivery.
• The framework supports future global developments in medical education technology.