ARTIFICIAL INTELLIGENCE IN THE SUICIDE PREVENTION - A SYSTEMATIC REVIEW

Objectives: Suicide remains a pressing global public health issue, claiming over 700,000 lives annually. Despite decades of research, predictive models for suicide risk have seen limited progress, emphasizing the need for innovative approaches. Artificial intelligence (AI) and machine learning (ML) are increasingly recognized for their potential to enhance early detection, risk assessment, and intervention strategies. The objective of this review is to provide a critical evaluation of the role of AI in the detection and prevention of suicide, with particular emphasis on its current implementations, potential benefits, and inherent limitations. Methods: A literature review was conducted for the period spanning 2020 to 2025. Relevant publications were identified through a systematic search of the PubMed database using the keywords: artificial, intelligence, suicide, and prevention. The search retrieved 271 articles, of which 23 met the predefined inclusion criteria and were incorporated into the final analysis. Key findings: Recent developments highlight how AI technologies can support suicide prevention through social media surveillance, clinical decision-making tools, and real-time crisis response systems. However, these advancements are not without challenges. Significant concerns persist around data privacy, algorithmic bias, transparency, and the erosion of human-centered care. Conclusions: While AI-driven tools offer substantial opportunities for suicide prevention, their integration into mental health care must be approached with caution. Ethical safeguards, clinical oversight, and continued research are essential to ensure these technologies complement—rather than compromise—traditional human care. A balanced, multidisciplinary approach is vital to realize the full potential of AI while maintaining patient trust, safety, and dignity.