Developing an Artificial Intelligence Chatbot to Promote HIV Testing

标题 开发人工智能聊天机器人以促进艾滋病毒检测 摘要（30行） 艾滋病毒检测启动了艾滋病毒预防和治疗护理的连续性。男性艾滋病毒检测水平， 与男性发生性关系（MSM）的比例在马来西亚等不同环境中尤其低。MSM越来越多的贡献 在高度污名化和歧视的情况下，艾滋病毒的传播加剧。对于高风险的MSM， 新的指南建议经常进行艾滋病毒检测，每3到6个月一次。然而，男男性行为者的艾滋病毒检测 通常由于个体原因而不太频繁地发生（例如，对风险披露的高度关注），诊所（例如， 违反保密规定，以及医疗保健提供者的歧视）和政策（同性恋的刑事化 性行为）障碍。在马来西亚，男男性接触者的艾滋病毒感染率飙升至21.6%，超过了吉隆坡的40.9%。 吉隆坡虽然对马来西亚符合PrEP标准的MSM进行的监测调查表明， 测试是70.3%，去年测试是40.9%，只有9.5%的测试超过一年，尽管非常 自我报告的风险水平。然而，一旦测试，马来西亚的艾滋病毒感染者可能会接受抗逆转录病毒治疗 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焦点。 创新的战略，激励和提供指导，在这些设置男男性行为者之间的测试是 因此迫切需要。信息-动机-行为技能（IBM）模型 行为改变非常适合于克服MSM中推荐的HIV检测的障碍。而且在 像马来西亚这样的地方，艾滋病毒的流行已经从主要集中在PWID转变为 在男男性接触者中的不稳定流行病，理论指导的行为改变策略，告知，激励和提供 更充分地参与建议的艾滋病毒检测的实用技能有望加速艾滋病毒预防 关怀的连续性。鉴于艾滋病毒检测存在许多个人、诊所和政策障碍，移动的医疗（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行为者，并促进推荐的艾滋病毒检测。最近在美国的研究， 中国、南非和秘鲁表明，使用智能手机和应用程序的移动医疗干预具有潜力 增加艾滋病毒检测，同时保持男男性接触者的保密性，并提供实时信息， 克服障碍的动机和行为技能。这种移动健康干预措施是可行的，也是可以接受的。 在MSM中，包括在马来西亚，大多数MSM使用智能手机应用程序找到性伴侣， 建议的干预措施的接口和功能。然而，目前的移动健康战略受到以下因素的限制： 它们缺乏自动化，需要高强度和持续的人力投入，这限制了它们的规模扩大。 使用机器学习的人工智能（AI/ML）可能会克服这样的限制，但尚未得到应用 到基于移动健康的艾滋病毒检测算法。因此，我们的目标是发展和试点测试，以评估可接受性， AI/ML聊天机器人相对于常规治疗（对照）进行HIV检测的可行性。时发现的问题 探索性试点研究将为未来的前瞻性1型混合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.