After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions. In addition, Alleviate's modular design and explainable decision-making lends itself to robust and continued feedback-based refinements to its design. In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better to facilitate optimal care strategies.

翻译：疫情后，人工智能（AI）支持的心理健康护理变得愈发重要。提供充分护理所需应对的重大挑战的广度与复杂性涉及：（a）个性化患者理解，（b）安全约束且经医学验证的聊天机器人患者交互，以及（c）基于聊天机器人-患者交互的持续反馈式设计改进支持。我们提出Alleviate，一个旨在通过个性化护理帮助遭受心理健康挑战的患者，并协助临床医生更好地理解其患者的聊天机器人。Alleviate从一系列公开可用的、临床有效的心理健康文本和数据库中获取信息，使其能够做出医学上合理且明智的决策。此外，Alleviate的模块化设计和可解释决策机制使其能够实现稳健且持续的反馈式设计改进。在本文中，我们阐述了Alleviate的不同模块，并提交了一段简短视频，展示Alleviate帮助患者与临床医生更好地相互理解以促进最优护理策略的能力。