Application of large language models as decision support tools in occupational health and safety management: a cohort study of industrial workers
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Published on: December 6, 2024
Francesca Vella1, Paola Senia2, Veronica Filetti1
1Department of Medicine and Surgery, Occupational Medicine, University of Enna Kore, Enna, Italy.
Background:
Occupational health and safety (OHS) risk assessment is a core preventive process aimed at identifying workplace hazards, estimating risks, and implementing control measures to reduce occupational injuries and diseases. Recent evidence indicates that AI-based systems may assist hazard identification, risk prioritization, and preventive planning, improving efficiency and standardization.
Objectives:
This study compared AI outputs with occupational physician (OP) analyses in risk assessment, health surveillance protocol drafting, and fitness-for-work determinations.
Methods:
This retrospective observational study was conducted in a multinational construction and facility management company with approximately 200 employees. An LLM-based system was evaluated for occupational risk assessment, health surveillance protocol development, and fitness-for-work decisions through structured comparison with an experienced OP. Three objectives were addressed: (1) analysis of the company risk assessment document (RAD); (2) comparison of surveillance protocols for specific tasks; (3) quantitative assessment of agreement in fitness-for-work judgments. The AI system (Perplexity Pro®, "Deep Research") was used. Agreement was measured using Cohen's Kappa.
Results:
AI-generated and OP-generated risk assessments were fully concordant (100%). Risk distribution across job categories was consistent, with high overall concordance (93%). Differences in surveillance protocols reflected regulatory interpretation and contextual exposure assessment rather than omission of clinically relevant elements.
Conclusion:
LLM-based AI can reliably support standardized occupational health decisions when applied to structured data. Despite high concordance in risk assessment, protocol development, and fitness-for-work judgments, regulatory interpretation and contextual clinical evaluation remain dependent on human expertise. AI should therefore be considered a complementary decision-support tool in occupational health practice.
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