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A bibliometric analysis of chatbot technology in healthcare: study protocol.
JMIR Research Protocols 2024 January 17
BACKGROUND: Chatbots have the potential to increase people's access to quality healthcare. However, the implementation of chatbot technology in healthcare system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.
OBJECTIVE: 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 utilization of chatbot technology for promoting health.
METHODS: In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplorer, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in healthcare. 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 five 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 employed in the development of health-related chatbots, along with their features and applications in healthcare settings. Software tool VOSViewer (Version 1.6.19, Leiden University, The Netherlands) will be used to construct and visualize bibliometric networks.
RESULTS: The preparation for the bibliometric analysis began on December 03, 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 three librarians at Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2,340 articles. The official search phase started on July 27, 2023. Our goal is to complete the screening of articles and the analysis by December 30, 2023.
CONCLUSIONS: Artificial intelligence chatbot, such as ChatGPT, has sparked numerous discussions within the healthcare industry regarding its impact on human health. Chatbot technology holds substantial promise for advancing healthcare systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with healthcare consumers, delivering personalized care, and providing accurate health-related information and knowledge remains a considerable challenge. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software employed in their development, and their preferred functionalities among users.
CLINICALTRIAL: N/a.
OBJECTIVE: 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 utilization of chatbot technology for promoting health.
METHODS: In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplorer, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in healthcare. 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 five 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 employed in the development of health-related chatbots, along with their features and applications in healthcare settings. Software tool VOSViewer (Version 1.6.19, Leiden University, The Netherlands) will be used to construct and visualize bibliometric networks.
RESULTS: The preparation for the bibliometric analysis began on December 03, 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 three librarians at Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2,340 articles. The official search phase started on July 27, 2023. Our goal is to complete the screening of articles and the analysis by December 30, 2023.
CONCLUSIONS: Artificial intelligence chatbot, such as ChatGPT, has sparked numerous discussions within the healthcare industry regarding its impact on human health. Chatbot technology holds substantial promise for advancing healthcare systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with healthcare consumers, delivering personalized care, and providing accurate health-related information and knowledge remains a considerable challenge. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software employed in their development, and their preferred functionalities among users.
CLINICALTRIAL: N/a.
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