Conclusions

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 AI has the power to transform workplace safety from a reactive system to a proactive, prevention-focused approach. Real-world results, such as those in the Chilean mining industry, show measurable improvements in safety outcomes. Nevertheless, technology is not a magic bullet—it must be guided by ethical standards, transparent policies, and worker involvement. The future of OHS lies in harmonizing technological innovation with human expertise, ensuring that progress never comes at the expense of dignity, trust, or safety.



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Background

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  Artificial Intelligence (AI) is no longer a futuristic concept—it is a present-day force redefining Occupational Health and Safety (OHS). By simulating human intelligence, AI can make complex decisions, detect patterns invisible to the human eye, and anticipate risks before they turn into accidents. From wearable devices that monitor heart rate and fatigue to predictive maintenance systems that prevent machinery breakdowns, AI is becoming a crucial ally in safeguarding workers. However, this technological shift is not without its challenges. As El-Helaly (2024) points out, while AI can enhance safety and productivity, it also raises questions about privacy, fairness, and its psychological impact on employees. The International Labour Organization (ILO, 2023) emphasizes that successful AI adoption requires balancing innovation with ethical responsibility and worker participation.
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Advantages and disadvantages

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   * Drawbacks Compared to Traditional Safety Methods: Traditional safety programs often rely on manual inspections, incident reporting, and reactive measures after accidents occur. While these methods have been effective for decades, they depend heavily on human observation and can miss subtle warning signs. AI, in contrast, offers continuous monitoring, instant data processing, and predictive capabilities—yet it introduces new challenges such as high implementation costs, data privacy concerns, and the risk of bias in algorithms. * Advantages of Using AI in OHS: Real-time health monitoring – Smart wearables and sensors continuously track workers’ vital signs, detect fatigue, and issue instant alerts about environmental hazards. Predictive hazard detection – AI systems process video, image, and sensor data to forecast unsafe conditions and prevent equipment malfunctions. Smart PPE (Personal Protective Equipment) – Helmets, gloves, and vests enhanced with AI adapt to changing ...
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Case Example: AI in Mining Safety

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 In 2022, a Chilean mining company introduced AI-powered sensors in helmets and vests to monitor temperature, air quality, and worker fatigue. Within the first year, the company reported a 15% reduction in heat-related incidents and an 8% decrease in near-miss accidents. This real-world case demonstrates how AI can provide timely alerts and data-driven interventions, improving both safety and productivity.
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