SynthesisInternational journal of environmental research and public health2020
Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.
Synthesis in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 12 of them syntheses 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
80 citing papers in PubMed, 12 syntheses or guidelines pooled it, 210 citations in OpenAlex.
- Can machine learning predict non-suicidal self-injury? A systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.Journal of medical systems · 2024Pooled it
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction.International journal of environmental research and public health · 2022Pooled it
- A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis.PloS one · 2021Pooled it
- Risk Model-Guided Clinical Decision Support for Suicide Screening: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.European psychiatry : the journal of the Association of European Psychiatrists · 2024Trial
- Liability and Standard of Care in AI-Driven Psychiatric Practice: European Viewpoint.Journal of medical Internet research · 2026Article
- Disseminating Health Prevention Programs Using AI-Crafted, Culturally Tailored Ads for Somali American Adolescents.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Modeling suicidal thoughts and behaviors in university students: integrating suicide theory with interpretable machine learning.BMC psychiatry · 2026Article
- Analysis of health infrastructure and suicide rates in Brazil: a nationwide ecological spatial-temporal study, 2009-2023.Lancet regional health. Americas · 2026Article
- Medical artificial intelligence and the right to health.BMC medical ethics · 2026Article
- Clinical Note-Extracted Psychosocial Factors for Predicting Suicide Attempt Among ED Patients With Suicidal Ideation.JAMA network open · 2026Article
20 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
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.
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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.