大模型在抑郁症筛查与诊断中的应用OACHSSCD

Depression is a common mental disorder that significantly impairs patients'social functioning and quality of life.In recent years,foundation models,with their powerful semantic understanding capability and multimodal data-processing capacity,have shown notable potential in the early screening and auxiliary diagnosis of depression.The construction of foundation model-based systems for depression screening and diagnosis typically involves four stages:data preprocessing,model selection,model training,and model evaluation.In these applications,foundation models primarily operate through contextualized semantic representation,attention mechanisms,multimodal behavioral capture,and predictive processing.Despite these advantages,their application still faces challenges such as algorithmic bias,insufficient diagnostic specificity,hallucination phenomena,privacy and security concerns,and ethical risks.In the future,the integration of foundation models into psychological intervention frameworks should be strengthened,with an emphasis on clinical translation pathways,in order to construct a more refined,dynamic,and culturally adaptive digital phenotype of depression,and to achieve the digital and intelligent transformation of mental health services.