ReviewCognitive neurodynamics2022
Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.
Review in Cognitive neurodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.Journal of medical systems · 2024Pooled it
- AI in Mental Health: Transforming Diagnosis and Management of Depression and Anxiety.Health science reports · 2026Article
- Advantages and disadvantages of artificial intelligence in the prediction and prevention of suicide.Postepy psychiatrii neurologii · 2026Review
- Identifying psychiatric manifestations in outpatients with depression and anxiety: a large language model-based approach.Npj mental health research · 2025Article
- Effectiveness of Mobile Applications for Suicide Prevention: A Systematic Review and Meta-Analysis.Behavioral sciences (Basel, Switzerland) · 2025Review
- Assessing the impact of AI on physician decision-making for mental health treatment in primary care.Npj mental health research · 2025Article
- [Research progress on the characteristics of magnetoencephalography signals in depression].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Depression Recognition Using Daily Wearable-Derived Physiological Data.Sensors (Basel, Switzerland) · 2025Article
- Development and Validation of a Nomogram for Predicting Suicidal Ideation Among Rural Adolescents in China.Psychology research and behavior management · 2024Article
- Evaluating deep learning techniques for identifying tongue features in subthreshold depression: a prospective observational study.Frontiers in psychiatry · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.