DETECTING THREATS TO HUMAN HEALTH AND SAFETY IN REAL-WORLD SITUATIONS USING ARTIFICIAL INTELLIGENCE: OPPORTUNITIES, CHALLENGES AND FUTURE DIRECTIONS

Abstract

In an era of rapid technological advancement, artificial intelligence (AI) is increasingly being harnessed in healthcare, occupational safety and public health to identify and mitigate threats to human health and safety in real-world situations. This article reviews current applications of AI for threat detection across clinical, workplace and public domains, analyses key benefits and risks, and outlines a conceptual framework for deploying AI in threat detection systems. We highlight major advances such as predictive analytics for adverse events, real-time monitoring of occupational hazards, and anomaly detection in health data. Simultaneously, we examine challenges including data bias, transparency, regulatory gaps, privacy concerns and system integration in complex settings. The paper proposes guidelines and future research directions to ensure AI-driven threat-detection systems are effective, ethically sound and resilient.