AI affective computing and behavioral health - Farsadaki, Vanessa; Griffy-Brown, Charla (Frontiers in Computer Science, 2026)
The integration of artificial intelligence (AI) and affective computing into behavioral health is transforming how mental wellbeing is assessed, monitored, and treated. As emotional and cognitive states can now be inferred through facial expressions, vocal tone, physiological signals, and behavioral cues, new technological paradigms are emerging to complement traditional approaches to mental healthcare. This is especially relevant in the wake of rising global mental health challenges, where access, personalization, and real-time feedback are essential components of effective care. Affective computing, a multidisciplinary field at the intersection of psychology, computer science, and cognitive science, seeks to enable machines to recognize, interpret, and respond to human emotions. When coupled with AI-driven data analytics and virtual reality (VR), it offers powerful tools for enhancing self-awareness, supporting clinical diagnostics, and delivering immersive therapeutic interventions. This paper explores how AI and affective computing can be leveraged across the behavioral health spectrum, from early detection and remote monitoring to therapy delivery and outcome prediction, with a particular emphasis on virtual environments as mediators of emotionally adaptive systems. We aim to review current innovations, examine their psychological validity, discuss ethical implications, and propose a research framework for advancing human-centered AI in behavioral health. Through this lens, we highlight the potential of emotionally intelligent systems not only to augment clinical practice but also to empower users in managing their mental wellbeing in real time.