ArticleTranslational psychiatry2020
Deep learning in mental health outcome research: a scoping review.
Article in Translational psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 3 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.
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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.
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Who cites it
90 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Can machine learning predict non-suicidal self-injury? A systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Identifying individuals at risk of developing psychosis: A systematic review of the literature in primary care services.Early intervention in psychiatry · 2023Pooled it
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- Review
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- Network structure of depression and anxiety symptoms with psychosocial factors among Chinese women with primary infertility: a multi-center cross-sectional study.BMC psychiatry · 2026Article
- Scoping review on natural language processing applications in counselling and psychotherapy.British journal of psychology (London, England : 1953) · 2026Article
- Graph theory reveals functional connectome disruptions in adolescent major depressive disorder with childhood trauma.Communications medicine · 2026Article
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- Artificial Intelligence in Couple and Family Therapy: Introduction of the AI Competencies.Journal of marital and family therapy · 2026Article
- Brain functional network connectivity interpolation characterizes the neuropsychiatric continuum and heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach.Artificial intelligence in medicine · 2026Article
- Deep learning based approach for Behavior classification in diagnoses of Autism Spectrum Disorder using naturalistic videos.Frontiers in computational neuroscience · 2026Article
- Mood states recognition based on Mandarin speech and deep learning in patients with bipolar disorder.BMC psychiatry · 2025Article
- Development and preliminary validation of mental health check-up questionnaire in Korea.BMC psychology · 2025Article
- Can Artificial Intelligence Enhance European Emerging Adults' Psychological Adjustment? A Scoping Review.Behavioral sciences (Basel, Switzerland) · 2025Review
- A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.Journal of biomedical semantics · 2025Article
- Article
- Deep learning identification of reward-related neural substrates of preadolescent irritability: A novel 3D CNN application for fMRI.Neuroimage. Reports · 2025Article
30 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
4 authors.
Funding
Abstract
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
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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.