Artificial Intelligence in Suicide Prevention: A Review

Recent breakthroughs in explainable artificial intelligence (XAI) have fundamentally transformed suicide risk prediction capabilities. Advanced machine learning models, particularly random forest algorithms employing SHAP interpretability techniques, have demonstrated exceptional predictive accuracy exceeding ۹۷% in identifying high-risk individuals. These systems provide clinicians with valuable insights through comprehensive analysis of critical psychological determinants including anger manifestations, social withdrawal patterns, and prior psychiatric hospitalization history. Concurrently, protective factors such as higher educational attainment and stable socioeconomic status have been reliably identified. However, significant implementation challenges persist, particularly concerning algorithmic biases, data privacy protection, and the need for culturally-adapted model architectures. The integration of natural language processing and multimodal data analytics promises to further enhance predictive precision. Crucially, these technological solutions must function as decision-support tools complementing - rather than replacing - clinical expertise and human judgment. The future development of this field hinges on creating sophisticated yet ethically-grounded solutions that harmonize cutting-edge computational methods with compassionate, human-centered care principles. This research underscores the imperative for robust ethical frameworks and comprehensive professional training to ensure responsible implementation of these predictive technologies in clinical practice.

کلیدواژه ها:

Explainable AI ، Suicide ، Machine Learning in Psychiatry