ReviewMolecular psychiatry2019
Deep neural networks in psychiatry.
Review in Molecular psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 4 of them syntheses that pooled it.
What it found
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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
100 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI).BMC psychiatry · 2025Pooled it
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders.Translational psychiatry · 2020Pooled it
- A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial.Translational psychiatry · 2026Trial
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- ECLAT based association rule mining for advancing workplace mental health and organizational insights.Scientific reports · 2026Article
- SHAP-based explainable machine learning analysis of reward-related neural connectivity to predict preadolescent irritability.Neuroimage. Reports · 2026Article
- Development of a machine learning-based mortality prediction model for patients with mental disorders and COVID-19.Frontiers in cellular and infection microbiology · 2026Article
- Machine learning algorithms and artificial neural networks for predicting schizophrenia using orbital parameters.Scientific reports · 2025Article
- The impact of the COVID-19 pandemic on the global burden of mental disorders: a counterfactual modeling study from 1990 to 2021.Translational psychiatry · 2025Article
- AIstain: Enhancing microglial phagocytosis analysis through deep learning.Cell reports methods · 2025Article
- Rapid thrombogenesis prediction in COVID-19 patients using DNN with data labeling.Scientific reports · 2025Article
- Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression.Molecular psychiatry · 2025Article
- Medication literacy and treatment adherence in people living with human immunodeficiency virus: Mediating effects of psychosocial factors.World journal of psychiatry · 2025Review
- Article
- Classification of Major Depressive Disorder Using Graph Attention Mechanism with Multi-Site rs-fMRI Data.Neuroinformatics · 2025Article
- Deep learning identification of reward-related neural substrates of preadolescent irritability: A novel 3D CNN application for fMRI.Neuroimage. Reports · 2025Article
- Health-Promoting Effects and Everyday Experiences With a Mental Health App Using Ecological Momentary Assessments and AI-Based Ecological Momentary Interventions Among Young People: Qualitative Interview and Focus Group Study.JMIR mHealth and uHealth · 2025Article
- Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures.Journal of clinical medicine · 2025Review
40 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them. We will then discuss and review directions along which (deep) neural networks can be, or already have been, applied in the context of psychiatry, and will try to delineate their future potential in this area. We will also comment on an emerging area that so far has been much less well explored: by embedding semantically interpretable computational models of brain dynamics or behavior into a statistical machine learning context, insights into dysfunction beyond mere prediction and classification may be gained. Especially this marriage of computational models with statistical inference may offer insights into neural and behavioral mechanisms that could open completely novel avenues for psychiatric treatment.
Indexed as
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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 OpenQuestion graph.