Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Int J Environ Res Public Health. 2020 Aug 15;17(16):5929. doi: 10.3390/ijerph17165929.


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.

Keywords: artificial intelligence; intervention; machine learning; prediction; risk; suicide.

Publication types

  • Research Support, N.I.H., Extramural
  • Systematic Review

MeSH terms

  • Artificial Intelligence*
  • Humans
  • Machine Learning*
  • Risk Assessment
  • Suicidal Ideation
  • Suicide Prevention*