Multimodal Generative AI for Personalized Therapeutic Art and Music Experiences
Keywords:
generative artificial intelligence; multimodal systems; therapeutic art; music therapy; personalization; sociotechnical infrastructure; AI governanceAbstract
The convergence of generative artificial intelligence and arts-based therapeutic practice is creating new possibilities for personalized mental health and wellness interventions. This paper presents a system-level analysis of multimodal generative AI platforms designed to deliver individualized therapeutic art and music experiences. Rather than focusing solely on algorithmic performance, the discussion emphasizes structural trade-offs in architecture, personalization, infrastructure, governance, deployment, robustness, fairness, and policy. The paper examines how text-to-image, text-to-audio, and cross-modal latent models can be integrated into coherent therapeutic systems while preserving clinical safety and user agency. It further analyzes the requirements for adaptive user modeling, privacy-preserving data infrastructure, multimodal alignment, and longitudinal engagement. Because therapeutic contexts introduce heightened ethical obligations, the paper considers how bias, accessibility, accountability, and regulatory compliance shape system design. The analysis situates generative therapeutic platforms within broader trends in foundation models, human-centered artificial intelligence, and digital health ecosystems. It argues that sustainable deployment depends not only on generative quality but also on institutional governance, interoperability with clinical workflows, auditability, and equitable access. The paper concludes with forward-looking perspectives on how multimodal generative AI can evolve into a responsible sociotechnical infrastructure for arts-based wellness without displacing human therapeutic judgment.
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