Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment

Machine learning can identify key suicide risk factors: The study found that anger problems, depression, and social isolation are the strongest predictors of suicide risk, providing psychiatrists with data-backed factors to prioritize in clinical assessments.

Explainability matters for clinical trust: By using SHAP (a technique that explains AI predictions), the researchers demonstrated that AI models can show why they make specific risk predictions, rather than operating as "black boxes"—a critical requirement for doctors to confidently use these tools in real clinical settings.

Protective factors are equally important: The analysis revealed that certain characteristics—including good income, respected occupations, and university education—were associated with lower suicide risk, suggesting that socioeconomic and social factors play a protective role that clinicians should consider.

High accuracy doesn't guarantee real-world readiness: While the Decision Tree model achieved 95.23% accuracy in this study, the paper's controlled environment may not reflect the messiness of actual clinical data, meaning further validation would be needed before deploying such tools in hospitals or mental health clinics.

AI should augment, not replace, clinical judgment: The authors frame these tools as assistants to help psychiatrists understand complex patterns in patient behavior, emphasizing that human expertise remains essential for responsible suicide risk assessment and intervention.