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

IF 3.3 Q2 ONCOLOGY
JMIR Cancer Pub Date : 2025-02-18 DOI:10.2196/66633
James C L Chow, Kay Li
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引用次数: 0

Abstract

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.

开发基于大型语言模型的医疗聊天机器人的有效框架:来自ChatGPT放射治疗教育的见解。
本观点提出了一个强大的框架,用于开发致力于放射治疗教育的医疗聊天机器人,强调准确性、可靠性、隐私性、伦理性和未来创新。通过分析现有研究,该框架评估了聊天机器人的性能,并确定了内容准确性、偏见和系统集成等挑战。研究结果强调了自然语言处理、个性化学习和沉浸式技术的发展机会。如果在设计时注重道德标准和可靠性,基于语言模型的大型聊天机器人可以显著影响放射治疗教育和医疗保健服务,将其定位为全球医学教育未来发展的宝贵工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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