Introduction: The use of chatbots in healthcare is an area of study receiving increased academic interest. As the knowledge base grows, the granularity in the level of research is being refined. There is now more targeted work in specific areas of healthcare, for example, chatbots for anxiety and depression, cancer care, and pregnancy support. The aim of this paper is to systematically review and summarize the research conducted on the use of chatbots in the field of addiction, specifically the use of chatbots as supportive agents for those who suffer from a substance use disorder (SUD). Methods: A systematic search of scholarly databases using the broad search criteria of (“drug” OR “alcohol” OR “substance”) AND (“addiction” OR “dependence” OR “misuse” OR “disorder” OR “abuse” OR harm*) AND (“chatbot” OR “bot” OR “conversational agent”) with an additional clause applied of “publication date” ≥ January 01, 2016 AND “publication date” ≤ March 27, 2022, identified papers for screening. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to evaluate eligibility for inclusion in the study, and the Mixed Methods Appraisal Tool was employed to assess the quality of the papers. Results: The search and screening process identified six papers for full review, two quantitative studies, three qualitative, and one mixed methods. The two quantitative papers considered an adaptation to an existing mental health chatbot to increase its scope to provide support for SUD. The mixed methods study looked at the efficacy of employing a bespoke chatbot as an intervention for harmful alcohol use. Of the qualitative studies, one used thematic analysis to gauge inputs from potential users, and service professionals, on the use of chatbots in the field of addiction, based on existing knowledge, and envisaged solutions. The remaining two were useability studies, one of which focussed on how prominent chatbots, such as Amazon Alexa, Apple Siri, and Google Assistant can support people with an SUD and the other on the possibility of delivering a chatbot for opioid-addicted patients that is driven by existing big data. Discussion/Conclusion: The corpus of research in this field is limited, and given the quality of the papers reviewed, it is suggested more research is needed to report on the usefulness of chatbots in this area with greater confidence. Two of the papers reported a reduction in substance use in those who participated in the study. While this is a favourable finding in support of using chatbots in this field, a strong message of caution must be conveyed insofar as expert input is needed to safely leverage existing data, such as big data from social media, or that which is accessed by prevalent market leading chatbots. Without this, serious failings like those highlighted within this review mean chatbots can do more harm than good to their intended audience.
Eur Addict Res

中文翻译：

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使用聊天机器人作为寻求物质使用障碍帮助的人的支持代理：系统回顾

简介：在医疗保健中使用聊天机器人是一个受到越来越多学术兴趣的研究领域。随着知识库的增长，研究级别的粒度正在细化。现在在医疗保健的特定领域有更有针对性的工作，例如，用于焦虑和抑郁、癌症护理和怀孕支持的聊天机器人。本文的目的是系统地回顾和总结在成瘾领域使用聊天机器人进行的研究，特别是使用聊天机器人作为物质使用障碍 (SUD) 患者的支持剂。方法：使用广泛的搜索标准（“药物”或“酒精”或“物质”）和（“成瘾”或“依赖”或“滥用”或“失调”或“滥用”或伤害*）对学术数据库进行系统搜索AND（“聊天机器人”或“机器人”或“会话代理”），附加条款应用“出版日期”≥ 2016 年 1 月 1 日和“出版日期”≤ 2022 年 3 月 27 日，确定要筛选的论文。系统评价和荟萃分析指南的首选报告项目用于评估纳入研究的资格，并采用混合方法评估工具来评估论文的质量。结果：搜索和筛选过程确定了 6 篇论文进行全面审查、2 篇定量研究、3 篇定性研究和 1 篇混合方法。这两篇定量论文考虑了对现有心理健康聊天机器人的改编，以扩大其为 SUD 提供支持的范围。混合方法研究考察了使用定制聊天机器人干预有害饮酒的效果。在定性研究中，一项基于现有知识和设想的解决方案，使用主题分析来衡量潜在用户和服务专业人员对在成瘾领域使用聊天机器人的投入。剩下的两项是可用性研究，其中一项侧重于聊天机器人（如 Amazon Alexa、Apple Siri、讨论/结论：该领域的研究语料库有限，鉴于所审查论文的质量，建议需要更多研究来更有信心地报告聊天机器人在该领域的实用性。其中两篇论文报告说，参与研究的人物质使用有所减少。虽然这是一个支持在该领域使用聊天机器人的有利发现，但必须传达一个强烈的警告信息，因为需要专家意见才能安全地利用现有数据，例如来自社交媒体的大数据，或通过流行方式访问的数据市场领先的聊天机器人。如果没有这一点，像本评论中强调的那些严重失败意味着聊天机器人对其目标受众弊大于利。
欧洲瘾君子研究所