How artificial intelligence is reshaping occupational safety and health
AI is increasingly being used to predict risks, prevent injuries, and support long-term safety decisions, while raising important questions around governance, ethics, and worker trust.
Artificial intelligence (AI) is becoming a central tool in occupational safety and health (OHS). This helps organizations move from reactive to early prediction and prevention. This paper analyzes the growing role of AI in detecting risks, preventing injuries, supporting long-term risk management, and addressing ethical challenges related to privacy, fairness, and worker trust. Based on case studies in manufacturing, construction, mining, logistics, agriculture, healthcare, and energy, this paper shows how AI-powered approaches can improve worker health and safety outcomes, reduce accident rates, and enhance organizational decision-making. This analysis highlights practical limitations, including data quality concerns and the dangers of over-reliance on automation. The conclusion argues that AI works best when combined with a strong safety culture, active management, transparent governance, and direct worker participation.
1. Introduction
Workplace safety and health programs have traditionally relied on injury records, inspections, and intermittent audits. Although these approaches are useful, they have limitations. Often, a hazard is only identified when a worker is injured or equipment malfunctions. AI offers another model built on continuous data collection, predictive analytics, and real-time intervention.
Across industries, employers are using AI to understand ergonomic strain, detect hazardous environmental conditions, predict equipment failure, and analyze long-term exposure risks. When applied responsibly, AI can provide workers and managers with a clearer picture of how risks unfold, augmenting rather than replacing human judgment. This paper examines the most common AI applications in occupational health and explains them using detailed case studies from different industrial sectors. (1-3)
2. Anticipate workplace risks
AI can help identify patterns early on that can lead to accidents, injuries, and chronic exposure problems.
2.1 Prediction model using historical data
AI can detect risk signals in large datasets. For example, you can correlate shift patterns with types of injuries and illnesses, or identify environmental conditions associated with increased accident rates.
Case Study A: Mining – Prediction of Rockfall Events
A mining company deployed AI to analyze seismic measurements, geological surveys, worker reports, and equipment vibration data across multiple underground sites. The model flagged patterns that predicted rockfall risk up to four hours in advance. This gave supervisors sufficient time to relocate crew members and adjust support arrangements. In the first year, potentially serious events decreased by more than half, and near-miss reports plummeted (4).
