Ni Zhao, Peng Mary L, Balakrishnan Vimala, Tee Vincent, Azwa Iskandar, Saifi Rumana, Nelson LaRon E, Vlahov David, Altice Frederick L
School of Nursing, Yale University, Orange, CT, United States.
Center for Interdisciplinary Research on AIDS, Yale University, New Haven, CT, United States.
JMIR Res Protoc. 2024 Feb 15;13:e54349. doi: 10.2196/54349.
Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.
This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health.
In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks.
The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024.
Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54349.
聊天机器人有潜力增加人们获得优质医疗保健的机会。然而，由于对健康和医疗环境中聊天机器人采用情况的出版物分析稀缺，聊天机器人技术在医疗保健系统中的实施情况尚不清楚。
本文提出一项文献计量分析方案，旨在让公众了解与使用聊天机器人技术促进健康相关的研究现状和新兴趋势。
在这项文献计量分析中，我们将从CINAHL、IEEE Xplore、PubMed、Scopus和Web of Science数据库中选择与聊天机器人技术及其在医疗保健中的应用相关的已发表论文。我们的搜索策略包括“聊天机器人”、“虚拟代理”、“虚拟助手”、“对话代理”、“对话式人工智能”、“交互式代理”、“健康”和“医疗保健”等关键词。五名既是人工智能工程师又是临床医生的研究人员将独立审查所选论文的标题和摘要，以确定其是否有资格进行全文审查。通讯作者（ZN）将担任调解人，以解决五名审稿人之间的任何差异和争议。我们的分析将涵盖聊天机器人研究的各种出版模式，包括年度出版物数量、其地理或机构分布以及支持聊天机器人研究的年度资助数量，并进一步总结与健康相关的聊天机器人开发中使用的方法，以及它们在医疗保健环境中的特点和应用。软件工具VOSViewer（版本1.6.19；莱顿大学）将用于构建和可视化文献计量网络。
文献计量分析的准备工作于2021年12月3日开始，当时研究团队开始熟悉本分析中可能使用的软件工具VOSViewer和CiteSpace，在此期间，他们就搜索词和初步结果咨询了耶鲁大学图书馆的三名馆员。在上述数据库上的初步搜索共产生了2340篇论文。正式搜索阶段于2023年7月27日开始。我们的目标是在2024年2月15日前完成论文筛选和分析。
诸如ChatGPT（OpenAI公司）之类的人工智能聊天机器人在医疗保健行业引发了众多关于其对人类健康影响的讨论。聊天机器人技术在推动全球医疗保健系统方面具有巨大潜力。然而，开发一个能够与医疗保健消费者进行精确交互、提供个性化护理并提供准确的健康相关信息和知识的复杂聊天机器人仍然是相当大的挑战。这项文献计量分析旨在填补现有文献中与健康相关的聊天机器人、其应用、开发中使用的软件及其在用户中的首选功能方面的知识空白。
国际注册报告标识符（IRRID）：PRR1-10.2196/54349。
