ARTIFICIAL INTELLIGENCE IN SUICIDE RISK PREDICTION: A STRUCTURED REVIEW OF MODEL PERFORMANCE, ETHICAL CHALLENGES, AND CLINICAL IMPLEMENTATION

Background: Suicide remains a critical global public health crisis, demanding proactive intervention strategies. Recent advancements in artificial intelligence (AI), including machine learning and natural language processing, offer unprecedented capabilities for early suicide risk prediction utilizing electronic health records, clinical notes, and digital phenotyping. Objective: This structured review evaluates current evidence regarding the performance of AI models in predicting suicide risk, analyzes the ethical challenges associated with their deployment, and identifies primary barriers and facilitators to successful clinical implementation. Methodology: A structured literature search was conducted utilizing PubMed/MEDLINE and Google Scholar for peer-reviewed articles published between 2018 and 2026. Following specific inclusion and exclusion criteria, the final corpus of studies was analyzed using a thematic synthesis framework focusing on predictive accuracy, ethical implications, and workflow integration. Results: While AI algorithms frequently demonstrate robust predictive accuracy, their clinical utility is heavily complicated by significant ethical and operational hurdles. Key challenges include algorithmic bias exacerbating health inequities, severe data privacy concerns, and a notable lack of clinician trust stemming from the "black box" nature of complex machine learning models. Furthermore, poor integration into existing psychiatric workflows limits real-world feasibility. Conclusion: AI represents a powerful, yet imperfect, socio-technical tool for suicide prevention. Bridging the translational gap from algorithmic validation to routine clinical use requires rigorous ethical governance, prioritized model explainability, and human-centered implementation strategies tailored to frontline healthcare settings.