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

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 challenge...

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Main Authors: James C L Chow, Kay Li
Format: Article
Language:English
Published: JMIR Publications 2025-02-01
Series:JMIR Cancer
Online Access:https://cancer.jmir.org/2025/1/e66633
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author James C L Chow
Kay Li
author_facet James C L Chow
Kay Li
author_sort James C L Chow
collection DOAJ
description 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.
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spelling doaj-art-111ed7faf714452aadf5ec8df7df50af2025-08-20T03:11:46ZengJMIR PublicationsJMIR Cancer2369-19992025-02-0111e6663310.2196/66633Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPTJames C L Chowhttps://orcid.org/0000-0003-4202-4855Kay Lihttps://orcid.org/0000-0002-5765-1635 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.https://cancer.jmir.org/2025/1/e66633
spellingShingle James C L Chow
Kay Li
Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT
JMIR Cancer
title Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT
title_full Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT
title_fullStr Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT
title_full_unstemmed Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT
title_short Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT
title_sort developing effective frameworks for large language model based medical chatbots insights from radiotherapy education with chatgpt
url https://cancer.jmir.org/2025/1/e66633
work_keys_str_mv AT jamesclchow developingeffectiveframeworksforlargelanguagemodelbasedmedicalchatbotsinsightsfromradiotherapyeducationwithchatgpt
AT kayli developingeffectiveframeworksforlargelanguagemodelbasedmedicalchatbotsinsightsfromradiotherapyeducationwithchatgpt