Systematic review and meta-analysis demonstrates superior accuracy of multimodal AI in depression screening, highlighting the clinical promise of combined behavioral and physiological tracking.
Depression is a prevalent and costly mental disorder across all ages. Artificial intelligence (AI)-assisted physiological and behavioral information-such as electroencephalography (EEG), eye movement, video or audio monitoring, and gait analysis-offers a promising tool for depression screening. We systematically reviewed the classification performance of these AI-assisted measures in depression screening. A comprehensive literature search was conducted in Google Scholar, Web of Science, and IEEE Xplore, with the search date up to June 7, 2025. The reported AUC values are pooled estimates calculated from all results of eligible studies. AI-assisted multi-modal methods achieved a pooled AUC of 0.95 (95% CI: 0.92-0.96), outperforming uni-modal methods (pooled AUC: 0.84-0.92). Subgroup analysis indicated deep learning models showed higher performance, with an AUC of 0.95 (95% CI: 0.93-0.97). These findings highlight the potential of AI-based multi-modal information in depression screening and emphasize the need to establish standardized databases and improve research design.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: