Background: A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective: The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods: We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results: The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions: The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

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中文翻译：

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基于人工智能的慢性病会话代理：系统文献综述。

背景：越来越多的对话代理或聊天机器人都配备了人工智能（AI）架构。它们在医疗保健应用中越来越普遍，例如为21世纪主要死亡原因之一的慢性病患者提供教育和支持的应用。基于AI的聊天机器人可与此类患者进行更有效，更频繁的互动。目的：本系统文献综述的目的是回顾专门针对慢性病设计的基于AI的对话代理的特征，医疗条件和AI体系结构。方法：我们使用PubMed MEDLINE，EMBASE，PyscInfo，CINAHL，ACM数字图书馆，ScienceDirect和Web of Science进行了系统的文献综述。我们使用“会话代理，”，“医疗保健”，“人工智能”及其同义词。我们使用Google警报更新了搜索结果，并筛选了其他相关文章的参考列表。我们纳入了涉及慢性病的预防，治疗或康复，涉及对话的媒介以及任何类型的AI体系结构的基础研究。由两名独立的审稿人进行筛选和数据提取，并使用Cohen kappa来衡量人际协议，并采用叙事方法进行数据合成。结果：文献检索发现2052篇文章，其中10篇符合纳入标准。少数已确定的研究以及准实验研究的盛行（n = 7）和聊天机器人的主要原型性质（n = 7）揭示了该领域的不成熟。据报道，聊天机器人解决了各种各样的慢性病（n = 6），显示了针对个别慢性病开发专门的对话代理的趋势。但是，在慢性疾病中以及慢性疾病之间缺乏对这些聊天机器人的比较。此外，所报告的评估措施尚未标准化，所解决的健康目标范围很广。这些研究特征共同使可比性变得复杂，并为将来的研究打开了空间。虽然自然语言处理代表了最常用的AI技术（n = 7），并且大多数对话代理都允许进行多模式交互（n = 6），但已确定的研究表明，其广泛的异质性，缺乏报道的AI技术和系统的深度以及不一致基础AI软件的分类法的使用，进一步加剧了研究结果的可比性和概括性。结论：关于基于AI的慢性病对话代理的文献很少，并且大部分由在原型阶段使用自然语言处理并允许多模式用户交互的聊天机器人进行的准实验研究组成。未来的研究可以从基于AI的对话代理的基于证据的评估以及在不同慢性健康状况之内和之间的比较中受益。除了更具可比性之外，通过更结构化的开发和标准化的评估流程，可以提高针对特定慢性病开发的聊天机器人的质量及其对目标患者的后续影响。基于AI的用于慢性病的对话代理的文献很少，并且大多由在原型阶段使用自然语言处理并允许多模式用户交互的聊天机器人进行准实验研究组成。未来的研究可以从基于AI的对话代理的基于证据的评估以及在不同慢性健康状况之内和之间的比较中受益。除了更具可比性之外，通过更结构化的开发和标准化的评估流程，可以提高针对特定慢性病开发的聊天机器人的质量及其对目标患者的后续影响。基于AI的用于慢性病的对话代理的文献很少，并且大多由在原型阶段使用自然语言处理并允许多模式用户交互的聊天机器人进行准实验研究组成。未来的研究可以从基于AI的对话代理的基于证据的评估以及在不同慢性健康状况之内和之间的比较中受益。除了更具可比性之外，通过更结构化的开发和标准化的评估流程，可以提高针对特定慢性病开发的聊天机器人的质量及其对目标患者的后续影响。未来的研究可以从基于AI的对话代理的基于证据的评估以及在不同慢性健康状况之内和之间的比较中受益。除了更具可比性之外，通过更结构化的开发和标准化的评估流程，可以提高针对特定慢性病开发的聊天机器人的质量及其对目标患者的后续影响。未来的研究可以从基于AI的对话代理的基于证据的评估以及在不同慢性健康状况之内和之间的比较中受益。除了更具可比性之外，通过更结构化的开发和标准化的评估流程，可以提高针对特定慢性病开发的聊天机器人的质量及其对目标患者的后续影响。

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