Assessing ChatGPT's Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study.

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES JMIR Medical Education Pub Date : 2024-08-07 DOI:10.2196/51157
Joseph C McBee, Daniel Y Han, Li Liu, Leah Ma, Donald A Adjeroh, Dong Xu, Gangqing Hu
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Abstract

Background: ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects.

Objective: The aim of this study was to evaluate ChatGPT's competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation.

Methods: We developed a model termed PanelGPT to assess ChatGPT's competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise.

Results: By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process.

Conclusions: ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education.

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评估 ChatGPT 在解决有关聊天机器人在运动康复中的应用的跨学科探索方面的能力:描述性分析。
公司背景ChatGPT 展示了卓越的对话能力和广泛的跨学科知识。此外,它还能在一个聊天会话中扮演多个角色。这种独特的多角色扮演功能使 ChatGPT 成为探索跨学科课题的一种有前途的工具:本研究旨在评估 ChatGPT 在处理跨学科探究方面的能力,使用一个案例研究来探索聊天机器人在运动康复领域应用的机遇和挑战:我们开发了一个名为 PanelGPT 的模型,通过模拟小组讨论来评估 ChatGPT 处理跨学科话题的能力。以聊天机器人在运动康复中的应用这一跨学科话题为例,我们通过 PanelGPT 让 ChatGPT 在模拟小组讨论中扮演理疗师、心理学家、营养学家、人工智能专家和运动员。在模拟过程中,我们向小组成员提出问题,而 ChatGPT 既扮演小组成员回答问题,又扮演主持人引导讨论。我们使用 ChatGPT-4 进行了模拟,并参考文献和我们的人类专业知识对回答进行了评估:结果:通过解决聊天机器人在运动康复中的应用所涉及的患者教育、物理治疗、生理学、营养学和伦理考虑等相关问题,ChatGPT 模拟小组讨论的回答合理地指出了聊天机器人的优势,如全天候支持、个性化建议、自动跟踪和提醒。它还正确地强调了患者教育的重要性,并指出了一些挑战,如有限的互动模式、情绪相关建议的不准确性、数据隐私和安全的保证、数据处理的透明度以及模型培训的公平性。它还强调,聊天机器人是作为副驾驶提供协助,而不是在康复过程中取代人类医疗保健专业人员:ChatGPT 通过模拟来自互补背景的多位专家,在解决跨学科探究方面表现出很强的能力,对协助医学教育具有重要意义:临床试验:不适用。
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来源期刊
JMIR Medical Education
JMIR Medical Education Social Sciences-Education
CiteScore
6.90
自引率
5.60%
发文量
54
审稿时长
8 weeks
期刊最新文献
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