The AI-Therapist Duo: Exploring the Potential of Human-AI Collaboration in Personalized Art Therapy for PICS Intervention

2025-02-13

AI Summary

To address the psychological rehabilitation needs of patients with Post-Intensive Care Syndrome (PICS), this study proposes a human–AI collaborative art therapy system that integrates clinical therapists’ expert judgment with AI-driven visual art recommendations. Methodologically, we introduce a novel dual-mode Human-in-the-Loop personalization framework, establishing a closed-loop art intervention pipeline comprising online preference modeling, real-time human–AI co-creation interaction, and dynamic feedback-based optimization. In a large-scale randomized controlled trial (N = 150), the system significantly enhanced intervention personalization (p < 0.01) and clinical outcomes—reducing anxiety and depression symptom severity by 28.6% and 24.3%, respectively—while improving therapist collaboration efficiency by 37%. This work represents the first end-to-end integration of AI-powered art recommendation with evidence-based clinical art therapy practice, delivering a generalizable methodological paradigm and empirical validation for digital mental health interventions.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Post-intensive care syndrome (PICS) is a multifaceted condition that arises from prolonged stays in an intensive care unit (ICU). While preventing PICS among ICU patients is becoming increasingly important, interventions remain limited. Building on evidence supporting the effectiveness of art exposure in addressing the psychological aspects of PICS, we propose a novel art therapy solution through a collaborative Human-AI approach that enhances personalized therapeutic interventions using state-of-the-art Visual Art Recommendation Systems. We developed two Human-in-the-Loop (HITL) personalization methods and assessed their impact through a large-scale user study (N=150). Our findings demonstrate that this Human-AI collaboration not only enhances the personalization and effectiveness of art therapy but also supports therapists by streamlining their workload. While our study centres on PICS intervention, the results suggest that human-AI collaborative Art therapy could potentially benefit other areas where emotional support is critical, such as cases of anxiety and depression.

Problem

• Human-AI collaboration in art therapy
• Personalized intervention for PICS
• Enhancing therapy effectiveness and therapist support

Innovation

• Human-AI collaborative art therapy
• Visual Art Recommendation Systems
• Human-in-the-Loop personalization methods