Machine Learning in Occupational Health and Safety: A Review of Knowledge Gaps

Machine learning is revolutionizing the way we work and the field of occupational health and safety (OHS) has significant knowledge gaps for its implementation. This review synthesizes current applications across hazard identification, risk assessment, ergonomics, PPE compliance monitoring, and environmental surveillance, while identifying critical areas for future research. Even with promising advances, challenges persist in developing machine learning models that work effectively across industries, integrate multi-modal data streams, and adapt to dynamic work environments. Key limitations include the need for more robust assessment tools, personalization capabilities, and solutions to data quality and privacy concerns. The field particularly lacks standardized frameworks for data collection and sharing, as well as clear ethical guidelines for implementing machine learning in workplace safety contexts. This analysis reveals promising research directions, including the development of explainable AI systems to support OHS decision-making, learning applications to address data scarcity, and privacy-preserving learning approaches. The integration of machine learning with internet-of-things (IoT) and extended reality technologies offers additional avenues for innovation. Advancing these opportunities requires interdisciplinary collaboration between OHS professionals, computer scientists, lawyers, and subject matter experts. This review concludes that realizing the full potential of machine learning in OHS depends on addressing both technical and organizational challenges. A focus on these identified research priorities in this field can make significant advances toward creating more effective, data-driven tactics to workplace safety and health management.