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Evaluating the ability of artificial intelligence to predict suicide: A systematic review of reviews
Salma Abdelmoteleb1, Muhammad Ghallab2, Waguih William IsHak3
1Cedars-Sinai Health System, Los Angeles, CA, USA.
Introduction:
Suicide remains a critical global public health issue, with approximately 800,000 deaths annually. Despite various prevention efforts, suicide rates are rising, highlighting the need for more effective strategies. Traditional suicide risk assessment methods often fall short in accuracy and predictive capability. This has driven interest in artificial intelligence (AI), particularly machine learning (ML), as a potential solution. This paper reviews systematic evaluations of AI's effectiveness in predicting suicide risk, aiming to explore AI's potential while addressing its challenges and limitations.
Methodology:
A meta-research approach was used to review existing systematic reviews on AI's role in suicide risk prediction. Following PRISMA guidelines, a comprehensive search was conducted in PubMed and Web of Science for publications from 2004 to 2024. Relevant studies were selected based on specific inclusion criteria, and data were extracted on review characteristics, AI techniques, outcomes, and methodological quality. The review focuses on AI/ML models predicting suicidal ideation (SI), suicide attempts (SA), and suicide deaths (SD) separately, excluding non-suicidal self-injury.
Results:
Out of 96 initial articles, 23 met the inclusion criteria for full-text review. Most studies focused on developing ML models to identify suicide risk, showing promising results in enhancing accuracy and effectiveness. These models utilize various data sources and analytical techniques. However, challenges remain, including high bias risk and issues with interpretability, which necessitate further validation and refinement of AI-driven methods.
Conclusion:
The review underscores the significant potential of AI, especially ML, in predicting suicide risk and attempts. Although ML models show promise, challenges like data limitations, bias, and interpretability issues need addressing. Continued research and ethical scrutiny are crucial to fully realize AI's potential in suicide prevention.
