A systematic review of multimodal depression detection: from technological pipelines to the clinical depression management cycle

The effective management of depression—a high-prevalence mental disorder defined by persistent low mood and complex psychosomatic symptoms—poses a significant global challenge. It is necessary to conduct objective, multi-source assessments throughout the entire depression management cycle (DMC), from early screening to long-term prognosis. Multimodal depression detection (MMDD) has emerged as a critical enabling technology that leverages artificial intelligence (AI) to integrate these multi-source data streams. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant search across major digital libraries (2015–2025; through Oct 23, 2025) and included 162 studies, synthesizing the technical pipeline—datasets, feature engineering, models, and fusion strategies. To unify fragmented perspectives, we introduce the AI–driven depression management cycle (AI-DMC), which maps MMDD applications onto two task paradigms: low-fidelity, large-scale monitoring (screening, prognosis) and high-fidelity, high-precision detection (diagnosis support, treatment assessment). Grounded in AI-DMC, we outline a clinically oriented roadmap and identify system-level gaps—data and annotation ecosystems, trustworthy fusion and interpretability, and translation from monitoring to clinical action—providing forward-looking guidance for research and deployment.