ArticleNPJ digital medicine2025
AI-assisted multi-modal information for the screening of depression: a systematic review and meta-analysis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electroencephalogram for the Diagnosis of Depression: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.Brain topography · 2026Pooled it
- Generative AI emotion recognition from bodily gestures and vocal tone reveals modality-specific performance and positivity bias.Scientific reports · 2026Article
- Speech as a biomarker for supported diagnosis of major depressive disorder using self-supervised representations.Nature communications · 2026Article
- Rethinking the prediction and prevention of dropout in digital mental health.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Influence of recurrence risk perception on depressive mood in first-time stroke patients and the mediating effect of coping styles and perceived discrimination.Scientific reports · 2026Article
- Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language.Frontiers in psychiatry · 2026Article
- An individualized risk prediction tool for ectopic pregnancy within the first 10 weeks of gestation based on machine learning algorithms.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.