Personalized Pathways for Artificial Intelligence-Powered Receptive Music Therapy Based on the “Three Categories of Etiological Factors System” Principle

Abstract

This study explores the theoretical framework for constructing personalized pathways in receptive music therapy by integrating the Traditional Chinese Medicine (TCM) principle of “Three Categories of Etiological Factors System” with artificial intelligence (AI) technology. The three dimensions—temporal appropriateness, geographical appropriateness, and individual appropriateness - offers a holistic approach to personalized healthcare that aligns with modern precision medicine. Through systematic analysis of how these three dimensions apply to music therapy practice, we propose a comprehensive AI-based multidimensional assessment model and dynamic adjustment mechanism. The research establishes a theoretical system encompassing three core modules: individual characteristic identification, environmental factor analysis, and temporal pattern recognition. This framework enables the intelligent selection and dynamic optimization of therapeutic music based on patients' physiological states, psychological profiles, cultural backgrounds, circadian rhythms, seasonal variations, and environmental contexts. By leveraging machine learning algorithms and deep learning technologies, the system can process multimodal data to generate highly personalized treatment protocols and adjust them in real-time according to therapeutic responses. This integration of ancient medical wisdom with cutting-edge AI technology provides novel theoretical perspectives and practical pathways for achieving precision-oriented and intelligentized music therapy, potentially enhancing treatment efficacy and accessibility across diverse clinical settings including mental health, neurological rehabilitation, pain management, and pediatric developmental disorders.