Developing an Artificial Intelligence Chatbot to Promote HIV Testing

标题 开发人工智能聊天机器人促进艾滋病毒检测 摘要(30行) 艾滋病毒检测启动了艾滋病毒预防和治疗护理连续体。男性中HIV检测水平 与男性发生性行为(MSM)和在马来西亚等不同环境中发生性行为的比例特别低。MSM的贡献越来越大 在高度耻辱和歧视的情况下，艾滋病毒传播加剧。对于高危男男性接触者， 新指南建议频繁进行艾滋病毒检测，每隔3至6个月进行一次。然而，男男性接触者中的艾滋病毒检测 通常由于个人(例如，对风险披露的高度关注)、临床(例如， 违反保密规定和医疗保健提供者的歧视)和政策(将同性定为刑事犯罪 性行为)障碍。马来西亚男男性接触者HIV感染率飙升至21.6%，吉隆坡超过40.9% 伦普尔。虽然在马来西亚对符合PrEP标准的MSM进行的监测调查表明，终身HIV 测试占70.3%，过去一年测试占40.9%，只有9.5%的测试超过一年一次，尽管非常出色 自我报告的风险水平。然而，一旦检测，马来西亚感染艾滋病毒的MSM很可能会接受ART治疗 并实现病毒抑制，使艾滋病毒检测成为艾滋病毒预防和治疗的中心重点。 激励和指导MSM在这些环境中进行测试的创新战略包括 因此迫切需要。使用信息-动机-行为技能(IBM)模型进行干预 行为改变非常适合于克服MSM中推荐的艾滋病毒检测的障碍。此外，在 像马来西亚这样的环境，艾滋病毒流行已经从主要集中在PWID过渡到 MSM的不稳定流行，以理论为指导的行为改变战略，告知、激励和提供 更全面地参与推荐的艾滋病毒检测的实用技能将加速艾滋病毒的预防 和护理连续体。鉴于艾滋病毒检测面临许多个人、诊所和政策障碍，移动医疗(MHealth) 减少“面对面”接触并提供行为技能菜单的干预措施非常适合增加 在高度污名化的环境中接触男男性接触者，并促进推荐的艾滋病毒检测。最近在美国的研究表明， 中国、南非和秘鲁表明，使用智能手机和应用程序的移动健康干预具有潜力 为了加强艾滋病毒检测，同时保持男男性接触者的机密性，并提供实时信息， 克服障碍的动力和行为技能。这样的mHealth干预是可行和可以接受的 在男男性接触者中，包括在马来西亚，大多数男男性接触者使用类似的智能手机应用程序找到性伴侣 拟议干预的接口和功能。然而，当前的移动健康战略受到以下限制 它们缺乏自动化，需要高强度和持续的人力投入，这限制了它们的扩大。 使用机器学习的人工智能(AI/ML)可能会克服这些限制，但尚未得到应用 基于移动健康的艾滋病毒检测算法。因此，我们的目标是开发和试行测试，以评估可接受性和 相对于常规治疗(对照)，AI/ML聊天机器人用于艾滋病毒检测的可行性。由此得出的结论 探索性的先导研究将为未来的类型1混合动力实施科学试验提供信息。

Title Developing an Artificial Intelligence Chatbot to Promote HIV Testing Abstract (30 Lines) HIV testing jumpstarts the HIV prevention and treatment care continuum. HIV testing levels in men who have sex with men (MSM) and in diverse settings like Malaysia are especially low. MSM increasingly contribute to heightened HIV transmission in the presence of high levels of stigma and discrimination. For high risk MSM, new guidelines recommend frequent HIV testing, ranging from every 3 to 6 months. Yet, HIV testing in MSM often occurs less frequently due to individual (e.g., heightened concerns about risk disclosure), clinic (e.g., confidentiality breaches, and discrimination from healthcare providers) and policy (criminalization of same-sex sexual behaviors) barriers. HIV prevalence in MSM has soared to 21.6% in Malaysia, exceeding 40.9% in Kuala Lumpur. While surveillance surveys of MSM in Malaysia who meet criteria for PrEP suggest that lifetime HIV testing is 70.3%, past-year testing is 40.9% and only 9.5% were tested more than once-yearly despite extraordinary levels of self-reported risk. Once tested, however, MSM with HIV in Malaysia are likely to be treated with ART and achieve viral suppression, making HIV testing a central focus for HIV prevention and treatment. Innovative strategies that motivate and provide guidance for testing among MSM in these settings are therefore urgently needed. Intervening using Information-Motivation-Behavioral Skills (IBM) model of behavioral change is ideally suited to overcome barriers to recommended HIV testing in MSM. Moreover, in settings like Malaysia where the HIV epidemic has transitioned from primarily concentrated in PWID to a volatile epidemic in MSM, theory-guided behavioral change strategies that inform, motivate and provide pragmatic skills to more fully engage in recommended HIV testing are poised to accelerate the HIV prevention and care continuum. Given the many individual, clinic and policy barriers to HIV testing, mobile health (mHealth) interventions that reduce “in person” contact and offer a menu of behavioral skills is ideally suited to increase access to MSM in highly stigmatized settings and promote recommended HIV testing. Recent studies in the U.S., China, South Africa, and Peru show that mHealth interventions using smartphones and apps have the potential to increase HIV testing while maintaining MSM’s confidentiality and providing real-time information, motivation and behavioral skills to overcome barriers. Such mHealth interventions are feasible and acceptable among MSM, including in Malaysia where most MSM find sexual partners using smartphone apps with similar interfaces and functionalities to the proposed intervention. Current mHealth strategies, however, are limited by their lack of automation and need for high-intensity and sustained human inputs, which restricts their scale-up. Artificial intelligence using machine learning (AI/ML) may overcome such limitations, but have yet to be applied to mHealth-based HIV testing algorithms. We therefore aim to develop and pilot test to assess acceptability and feasibility of an AI/ML-Chatbot for HIV testing relative to treatment as usual (control). Findings from this exploratory pilot study will inform a future prospective Type 1 Hybrid implementation science trial.