British Columbia Centre on Substance Use, Vancouver, BC, Canada.
Graduate Programs in Rehabilitation Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Am J Drug Alcohol Abuse. 2021 Jan 2;47(1):26-42. doi: 10.1080/00952990.2020.1817466. Epub 2020 Oct 2.
With the artificial intelligence (AI) paradigm shift comes momentum toward the development and scale-up of novel AI interventions to aid in opioid use disorder (OUD) care, in the identification of overdose risk, and in the reversal of overdose.
As OUD-specific AI interventions are relatively recent, dynamic, and may not yet be captured in the peer-reviewed literature, we conducted a review of the gray literature to identify literature pertaining to OUD-specific AI interventions being developed, implemented and evaluated.
Gray literature databases, customized Google searches, and targeted websites were searched from January 2013 to October 2019. Search terms include: AI, machine learning, substance use disorder (SUD), and OUD. We also requested recommendations for relevant material from experts in this area.
This review yielded a total of 70 unique citations and 29 unique interventions, which can be sub-divided into five categories: smartphone applications (n = 12); healthcare data-related interventions (n = 7); biosensor-related interventions (n = 5); digital and virtual-related interventions (n = 2); and 'other', i.e., those that cannot be classified in these categories (n = 3). While the majority have not undergone rigorous scientific evaluation via randomized controlled trials, several AI interventions showed promise in aiding the identification of escalating opioid use patterns, informing the treatment of OUD, and detecting opioid-induced respiratory depression.
This is the first gray literature synthesis to characterize the current 'landscape' of OUD-specific AI interventions. Future research should continue to assess the usability, utility, acceptability and efficacy of these interventions, in addition to the overall legal, ethical, and social implications of OUD-specific AI interventions.
随着人工智能 (AI) 范式的转变，人们越来越倾向于开发和扩大新型 AI 干预措施，以帮助治疗阿片类药物使用障碍 (OUD)，识别药物过量风险，并逆转药物过量。
由于针对 OUD 的 AI 干预措施相对较新、动态且可能尚未在同行评审文献中收录，因此我们对灰色文献进行了综述，以确定正在开发、实施和评估的针对 OUD 的特定 AI 干预措施的文献。
从 2013 年 1 月至 2019 年 10 月，我们检索了灰色文献数据库、定制的 Google 搜索和目标网站。搜索词包括：人工智能、机器学习、物质使用障碍 (SUD) 和 OUD。我们还要求该领域的专家推荐相关材料。
本次综述共产生了 70 篇独特的参考文献和 29 项独特的干预措施，可细分为五类：智能手机应用程序 (n = 12)；与医疗保健数据相关的干预措施 (n = 7)；生物传感器相关干预措施 (n = 5)；数字和虚拟相关干预措施 (n = 2)；以及“其他”，即无法归入这些类别的干预措施 (n = 3)。虽然大多数干预措施尚未通过随机对照试验进行严格的科学评估，但一些 AI 干预措施在帮助识别不断加剧的阿片类药物使用模式、为 OUD 治疗提供信息以及检测阿片类药物引起的呼吸抑制方面显示出了希望。
这是首次对针对 OUD 的特定 AI 干预措施进行的灰色文献综合分析。未来的研究应继续评估这些干预措施的可用性、实用性、可接受性和疗效，以及针对 OUD 的特定 AI 干预措施的整体法律、伦理和社会影响。
