How Artificial Intelligence Is Reshaping Occupational Health and Safety
AI is increasingly used to predict risks, prevent injuries, and support long-term safety decision-making, while raising important questions about governance, ethics, and worker trust.
- By Bernard Fontaine
- Dec 22, 2025
Artificial intelligence (AI) is becoming a central tool in occupational health and safety (OHS). It helps organizations shift from an after-the-fact response 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. Drawing on case studies from manufacturing, construction, mining, logistics, agriculture, healthcare, and energy, the paper shows how AI-enabled approaches can improve worker health and safety outcomes, reduce incident rates, and enhance organizational decision-making. The analysis highlights practical limitations, including data quality concerns and the danger of overreliance on automation. The conclusion argues that AI works best when paired with a strong safety culture, engaged management, transparent governance, and direct worker participation.
1. Introduction
Workplace health and safety programs have traditionally depended on injury logs, inspections, and intermittent audits. These approaches are useful but limited: they often identify hazards only after workers are harmed or equipment fails. AI offers a different model—one built on continuous data collection, predictive analysis, and real-time intervention.
Across industries, employers are using AI to understand ergonomic strain, detect hazardous environmental conditions, predict equipment breakdowns, and analyze long-term exposure risks. When applied responsibly, AI enhances—not replaces—human judgment by giving workers and managers clearer visibility into how risks evolve. This paper examines the most common AI applications in occupational health and illustrates them with detailed case studies from varied industrial sectors. (1-3)
2. Predicting Workplace Risks
AI enables early identification of patterns that precede accidents, injuries, and chronic exposure problems.
2.1 Predictive Models Using Historical Data
AI can detect risk signals within large datasets—for example, linking shift patterns with injury and illness types or identifying environmental conditions associated with higher incident rates.
Case Study A: Mining – Predicting Rockfall Events
A mining company deployed AI to analyze seismic readings, geological surveys, worker reports, and equipment vibration data across several underground sites. The model flagged patterns predicting rockfall hazards up to four hours in advance. This gave supervisors enough time to relocate crews and adjust support structures. In the first year, potentially severe events decreased by more than half, and near-miss reports declined sharply.(4)
Case Study B: Healthcare – Predicting Needle-Stick Injuries
A hospital system used machine-learning models to analyze:
- Patient acuity
- Staffing levels
- Time of day
- Nurse turnover rates
- Incident logs
The model predicted when needle-stick injuries were more likely, especially during overnight hours with high patient loads. After staffing changes and workflow adjustments, incidents dropped noticeably.(5)
2.2 Predictive Maintenance and Equipment Failure
Mechanical failures often precede workplace injuries. AI-based predictive maintenance helps prevent equipment-related accidents.
Case Study C: Manufacturing – CNC Machine Failure Prevention
A machining facility installed sensors on its CNC mills to track heat, vibration, feed rates, and tool wear. The AI system noticed a vibration signature associated with spindle fatigue weeks before operators noticed performance changes. Repairs were scheduled early, avoiding a probable spindle failure that could have resulted in fragments ejecting at high velocity.(6)
3. Prevention: Real-Time Monitoring and Worker Protection
Predicting risk is only half the challenge. AI can also intervene during work to prevent injuries before they occur.
3.1 Wearables for Ergonomics, Fatigue, and Physiological Stress
Wearables can measure movement patterns, posture, heat load, heart rate, and physical strain.
Case Study D: Logistics – Safe Lifting Through Wearable Sensors
A distribution center used smart belts with motion sensors that alerted workers when they used unsafe lifting techniques. Over six months:
- Unsafe lifts dropped dramatically
- Lower-back injuries declined
- New workers were able to learn proper form faster
Workers reported feeling supported rather than policed because the data was not used for discipline.(7)
Case Study E: Agriculture – Heat Illness Prevention
Field workers wore small sensors tracking heat stress indicators. The AI system combined sensor data with weather forecasts and irrigation schedules to determine when heat limits would be exceeded. Supervisors adjusted break schedules and introduced shaded rest areas. Heat-related illnesses fell substantially over the growing season.(8)
3.2 Computer Vision for Unsafe Conditions and Behavior
Camera-based systems can detect patterns indicative of unsafe actions or hazardous conditions.
Case Study F: Construction – Hazard Detection on Job Sites
A contractor installed AI-enabled cameras that identified:
- Missing hard hats
- Proximity to heavy equipment
- Unstable scaffolding
- Workers entering restricted zones
Instead of issuing penalties, supervisors used the data to adjust workflows, add barriers, and improve communication. Within months, near-miss reports dropped, and rework related to safety violations declined, saving both time and cost.(1)
3.3 Environmental Monitoring for Exposure Control
AI systems can detect harmful exposure noise, dust, gas leaks, or chemical concentrations—and trigger interventions.
Case Study G: Energy Sector – Gas Leak Detection at Refineries
A refinery implemented AI-based detection that combined infrared imaging with real-time gas sensors. The system identified small methane leaks that human inspectors missed. Early detection helped reduce worker exposure and prevent potential fires.(9)
4. Long-Term Risk Management
AI’s long-term value lies in its ability to reveal chronic risks and inform strategic decisions.
4.1 Identifying Recurring Injury Patterns
Long-term data analytics allow organizations to track trends across time, location, shifts, and job roles.
Case Study H: Food Processing – Repetitive Strain Patterns
A food processing company reviewed three years of musculoskeletal injury data. AI linked injuries to specific production lines where the belt speed and task cycle created unusually high repetition rates. Redesigning workstations and adjusting line speed lowered injury rates and improved productivity.(10)
4.2 Tailored Training and Decision Support
AI highlights specific skill gaps and helps shape targeted training programs.
Case Study I: Aviation – Tailored Ramp Safety Training
An airline fed safety reports, sensor data from ground vehicles, and aircraft turnaround logs into an AI model. The system identified behaviors linked to minor collisions and near misses. Training programs were tailored to these patterns, reducing airside incidents significantly.(11)
5. Ethical, Legal, and Governance Challenges
AI promises major benefits but raises serious questions.
5.1 Worker Privacy and Surveillance Concerns
Workers often worry about being monitored unfairly. Programs succeed when employers:
- Explain data collection clearly
- Ensure transparency
- Separate safety data from performance metrics
- Allow workers to see their own data
Without these steps, worker resistance is common.
5.2 Data Quality and Algorithmic Bias
AI is only as good as the data it receives. Underreporting, sampling bias, and inconsistent recordkeeping can distort predictions.
5.3 Overreliance on AI for Safety Decisions
AI is a tool, not a replacement for:
- Training
- Supervision
- Human judgment
- Established safety protocols
Systems must support—not override—qualified professionals.
6. Discussion
Across case studies, several themes emerge:
- AI works best when used for prevention rather than punishment.
Workers engage more when they feel supported.
- Cross-functional collaboration is essential.
Safety and health teams, IT, operations, and workers must design systems together.
- AI is most effective when paired with a strong safety culture.
Tools cannot compensate for understaffing, poor communication, or a lack of leadership involvement.
- Early, small successes improve adoption.
Starting with targeted pilot programs builds trust and demonstrates value.
7. Conclusion
Artificial intelligence has the potential to transform occupational health by identifying risks earlier, preventing injuries, and giving organizations deeper insight into long-term trends. When implemented transparently and ethically, AI supports workers, enhances decision-making, and strengthens safety culture across industries. The most successful applications combine technology with human expertise. AI sets the stage, but people still play the central role—interpreting data, building trust, and shaping the workplace environments where safety decisions are made.
References
- Choudhary A, Tiwari MK. Computer vision in occupational safety: a review. Safety Science. 2021;139.
- European Agency for Safety and Health at Work (EU-OSHA). The Impact of Artificial Intelligence on Occupational Safety and Health. 2019.
- McAfee A, Brynjolfsson E. Machine, Platform, Crowd: Harnessing Our Digital Future. New York: W.W. Norton; 2017.
- Roberts M, O’Neill J. AI and hazard prediction in underground mining. Journal of Mine Safety Engineering. 2020;44(2).
- Kutcher R, et al. Predicting needle-stick injuries using machine learning. Journal of Occupational Health. 2019;61(5).
- Zhang L, et al. Predictive maintenance and worker safety in smart manufacturing environments. International Journal of Industrial Ergonomics. 2022;87.
- Silva D, Hartwell J. Wearable ergonomic monitors for lifting safety: outcomes and challenges. Applied Ergonomics. 2020;82.
- Nguyen T, et al. Heat stress prediction models for outdoor agricultural work. Journal of Agromedicine. 2020;25(3).
- Walton S, Davis P. AI applications in the energy sector: health and safety implications. Process Safety and Environmental Protection. 2021;150.
- Lee A, Ramirez J. Repetitive strain injuries in food manufacturing: AI-based analysis. Occupational Health Insights. 2022;14.
- Fernandez P, Clarke B. AI-enabled ramp safety programs in aviation. Journal of Airport Operations and Safety. 2021;12(4).