Family Medicine and Primary Care, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore.
Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom.
J Med Internet Res. 2020 Aug 7;22(8):e17158. doi: 10.2196/17158.
Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care.
This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application.
We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms "conversational agents," "conversational AI," "chatbots," and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.
The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education.
The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence-driven, and smartphone app-delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents' formats, focusing on their acceptability, safety, and effectiveness.
会话代理，也称为聊天机器人，是旨在模拟人类文本或口头对话的计算机程序。它们越来越多地用于包括医疗保健在内的多个领域。通过提高可及性、个性化和效率，会话代理有可能改善患者护理。
本研究旨在综述当前医疗保健中会话代理的应用、差距和文献中的挑战，并为其未来的研究、设计和应用提供建议。
我们进行了范围界定审查。在 2019 年 4 月，通过 MEDLINE（在线医学文献分析和检索系统；Ovid）、EMBASE（医学文摘数据库；Ovid）、PubMed、Scopus 和 Cochrane Central 数据库使用“会话代理”、“会话 AI”、“聊天机器人”和相关同义词进行了广泛的文献检索。我们还使用 OCLC（联机计算机图书馆中心）WorldCat 数据库和 ResearchGate 等来源在灰色文献中进行了搜索。检查了相关文章的参考文献列表以获取其他文章。由 2 名审查员同时进行筛选和数据提取。通过采用主题分析的原则，对纳入的证据进行了叙述性分析。
文献检索产生了 47 份符合纳入标准的研究报告（45 篇文章和 2 项正在进行的临床试验）。确定的会话代理主要通过智能手机应用程序（n=23）提供，并且仅使用自由文本作为主要输入（n=19）和输出（n=30）模式。描述聊天机器人开发的案例研究（n=18）最为普遍，仅确定了 11 项随机对照试验。文献中报道最多的 3 种会话代理应用是治疗和监测、医疗保健服务支持和患者教育。
医疗保健中关于会话代理的文献主要是描述性的，旨在治疗和监测以及医疗服务支持。它主要报告基于文本、人工智能驱动、智能手机应用程序交付的会话代理。迫切需要对各种医疗保健会话代理的格式进行稳健评估，重点关注其可接受性、安全性和有效性。
