PDF(Pubmed)
Abstract：
Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases.
A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools.
Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses.
The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.
A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools.
Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses.
The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.
摘要：
■在聊天机器人中利用人工智能（AI），尤其是慢性疾病,变得越来越普遍。这些人工智能驱动的聊天机器人是增强患者沟通的重要工具。解决慢性病患病率上升的问题，并满足对支持性医疗保健应用日益增长的需求。然而,在学术文献中，评估人工智能驱动的聊天机器人干预对医疗保健影响的综合评论存在显著差距。本研究旨在评估用户满意度，干预效果，以及为慢性病设计的聊天机器人系统的具体特征和人工智能架构。
■通过采用诸如PubMedMEDLINE之类的不同数据库，对现有文献进行了彻底的探索。CINAHL,EMBASE,PsycINFO,ACM数字图书馆和Scopus。本分析中包含的研究包括在预防的背景下使用聊天机器人或其他形式的AI架构的主要研究，治疗或康复慢性病。使用Riskof2.0工具进行偏倚风险评估。
■获得了784个结果，随后,8项研究符合纳入标准.干预方法包括健康教育(n=3)，行为变化理论(n=1)，压力和应对(n=1)，认知行为治疗(n=2)和自我护理行为(n=1)。这项研究为人工智能聊天机器人在处理各种慢性病方面的有效性和用户友好性提供了有价值的见解。总的来说,用户对这些聊天机器人自我管理慢性疾病表现出良好的接受度。
■审查的研究表明，有希望接受AI驱动的聊天机器人来自我管理慢性病。然而,由于技术文件不足,有关其疗效的证据有限,因此需要未来的研究提供详细的描述并优先考虑患者的安全性.这些聊天机器人采用自然语言处理和多模式交互。后续研究应侧重于基于证据的评估，促进不同慢性健康状况的比较。
■通过采用诸如PubMedMEDLINE之类的不同数据库，对现有文献进行了彻底的探索。CINAHL,EMBASE,PsycINFO,ACM数字图书馆和Scopus。本分析中包含的研究包括在预防的背景下使用聊天机器人或其他形式的AI架构的主要研究，治疗或康复慢性病。使用Riskof2.0工具进行偏倚风险评估。
■获得了784个结果，随后,8项研究符合纳入标准.干预方法包括健康教育(n=3)，行为变化理论(n=1)，压力和应对(n=1)，认知行为治疗(n=2)和自我护理行为(n=1)。这项研究为人工智能聊天机器人在处理各种慢性病方面的有效性和用户友好性提供了有价值的见解。总的来说,用户对这些聊天机器人自我管理慢性疾病表现出良好的接受度。
■审查的研究表明，有希望接受AI驱动的聊天机器人来自我管理慢性病。然而,由于技术文件不足,有关其疗效的证据有限,因此需要未来的研究提供详细的描述并优先考虑患者的安全性.这些聊天机器人采用自然语言处理和多模式交互。后续研究应侧重于基于证据的评估，促进不同慢性健康状况的比较。
