Ran Li, Jianchuan Chi

Ran Li

May 06, 2026

Artificial intelligence, Depression, Clinical management.

As one of the psychiatric disorders with the heaviest disease burden globally, the clinical management of depression faces the "three lows" dilemma: low detection rate, low treatment rate, and high relapse rate. The development of Artificial Intelligence (AI) technology offers a new paradigm to address this predicament. This paper systematically analyzes the current application status and technical pathways of AI across the entire "screening-diagnosis-treatment-management" process of depression: In the early identification and screening stage, AI integrates scale data, physiological indicators, digital footprints, and multimodal information, significantly enhancing early warning capabilities. In the diagnosis and differential diagnosis stage, AI assists structured interviews and quantitative assessments, combined with neuroimaging and peripheral biomarkers to achieve precise differentiation. In the treatment domain, AI promotes individualized medication decisions, digital psychotherapy, and parameter optimization for physical treatments. In the prognosis management and relapse prevention stage, through remote monitoring, dynamic relapse risk prediction, and self-management support systems, AI facilitates a shift from intermittent follow-up to continuous monitoring. The role of AI should be positioned as "augmented intelligence." The future integrated model of "human-AI collaboration" is expected to drive mental health services towards a more predictive, preventive, personalized, and participatory direction, ultimately serving patient well-being and dignity.

© 2026, the Authors. Published by ISCCAC

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