Artificial Intelligence, Machine Learning Approach and Suicide Prevention: A Qualitative Narrative Review

Abstract

The initial step in suicide prevention involves identifying individuals who may be at risk of attempting suicide at an early stage. Utilising artificial intelligence (AI) and machine learning (ML) techniques offers innovative avenues for the early detection of such individuals. This qualitative narrative review examines the current state of AI and ML applications in suicide prevention.

Introduction

Suicide remains a significant public health challenge worldwide. Traditional assessment methods, while valuable, have limitations in predicting suicidal behavior. The integration of AI and ML technologies presents new opportunities for enhancing early detection and intervention strategies.

Literature Review

Our comprehensive review identified several key areas where AI and ML are being applied in suicide prevention: - Risk assessment algorithms - Natural language processing for social media monitoring - Predictive modeling using electronic health records - Mobile health applications with AI-driven interventions

Findings

The review reveals promising applications of AI and ML in suicide prevention, including improved accuracy in risk prediction and the ability to process large datasets to identify at-risk individuals. However, challenges remain in terms of ethical considerations, data privacy, and the need for human oversight.

Conclusion

AI and ML technologies show significant potential in enhancing suicide prevention efforts. Future research should focus on developing robust, ethical, and clinically validated AI systems that can support healthcare professionals in identifying and helping at-risk individuals.