Based on public datasets, this study designs a simulation experiment to explore the potential impact of an integrated treatment framework incorporating artificial intelligence (AI) on the long-term mental health of patients with depression. Different from previous studies that focused on a single intervention method, this paper proposes combining AI technology with music therapy and herbal therapy to construct an individualized intervention optimization and feedback system. During the sixmonth simulated intervention, the system evaluates the effects of different treatment pathways on psychological assessment indicators such as Patient Health Questionnaire-9 (PHQ-9) and Hamilton Depression Rating Scale (HAMD). The results show that the integrated treatment group is significantly superior to the control group in the main scoring indicators. This study verifies the feasibility of applying AI to the integration of multimodal treatment strategies under non-clinical conditions and provides new ideas for personalized intervention in depression.
Chen et al. (Sat,) studied this question.