Scalable Clinical Informatics Frameworks for AI-Enabled Assistive Systems in Mental Health Care

Abstract

Mental health disorders are encountering a significant increase globally. There are several initiatives and programs designed to improve mental health care systems. However, mental health care systems face several persistent challenges, including access, growing demand, and workforce shortage. Employing Artificial Intelligence (AI)-enabled assistive systems, such as socially assistive robots and virtual agents, provides promising support through coaching, structured therapeutic guidance, and companionship. Despite the promising results, it is challenging to adopt these systems at scale due to their cost, deployment complexity, and the lack of scalable clinical informatics frameworks to guide real-world implementation. This paper proposes a clinical informatics framework for the scalable, cost-effective deployment of AI-enabled assistive systems in mental health care. The proposed framework emphasizes task characterization based on clinical risk, embodiment selection, evaluation metrics, and governance and safety considerations aligned with clinical workflow.