Ran Li, Jianchuan Chi

Ran Li

May 06, 2026

Artificial intelligence, Depression, Clinical management.

Depression, as one of the mental disorders with the heaviest global disease burden, is confronted with the "three lows" predicament of low recognition rate, low treatment rate and high recurrence rate in clinical management. The development of artificial intelligence (AI) technology has provided a brand-new paradigm for breaking this deadlock. The application status and technical paths of AI in the entire process of "screening - diagnosis - treatment - management" of depression were systematically explored: In the early identification and screening stage, AI integrates scale data, physiological indicators, digital footprints and multimodal information, significantly enhancing the early warning capability; In the diagnosis and differentiation stage, AI-assisted structured interviews and quantitative evaluations, combined with neuroimaging and peripheral biomarkers, achieve precise differentiation. In the field of therapy, AI has driven individualized medication decisions, digital psychotherapy, and parameter optimization in physical therapy. In the prognosis management and recurrence prevention stage, AI has achieved a transformation from intermittent follow-up to continuous monitoring through remote monitoring, dynamic recurrence risk prediction, and self-management support systems. The role of AI should be positioned as "enhancing intelligence". In the future, the integrated model of "human-machine collaboration" is expected to drive mental health services towards a more predictive, preventive, personalized and participatory direction, ultimately serving the well-being and dignity of patients.

© 2026, the Authors. Published by ISCCAC

This is an open access article distributed under the CC BY-NC license