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

computational-biology
artificial-intelligence
natural-language-and-speech
sentiment-analysis
neural-networks

Abstract

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 PRISMA-compliant search across major digital libraries (2015–2025; through Sep 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.
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