A Conceptual Framework for Integrating AI into Risk-Based Hazard Management and Auditing in Occupational Safety and Health Systems
Keywords:
Artificial Intelligence (AI), Occupational Safety and Health (OSH), Predictive Hazard Assessment, Risk-Based Management, Safety Audit, AssuranceAbstract
Purpose: This study examines how artificial intelligence (AI) can enhance occupational safety and health (OSH) by integrating operational risk management and auditing within a unified, risk-based framework. It addresses the gap in understanding AI’s role across the OSH lifecycle. Methods: A conceptual framework was developed based on ISO 45001:2018 principles, combining AI-enabled hazard identification, predictive risk assessment, control implementation, and feedback-driven review. Key features include machine learning analytics, sensor-based monitoring, and decision-support systems. The framework was designed by considering organizational conditions, control measures, and evidence-based decision-making, with attention to traceability and auditability. Results: The framework specifies an AI-enabled OSH lifecycle integrating hazard identification, predictive risk evaluation, control implementation, and feedback-driven review within a unified risk-based structure. It establishes traceable linkages between risk signals, control measures, and audit evidence, and defines data flows supporting continuous monitoring, anomaly detection, and risk-informed decision-making across operational and assurance functions. Implications: Integrating AI into OSH practice supports preventive, data-driven safety management, enhances certification assurance, and informs governance and policy decisions, while emphasizing responsible implementation. Conclusion: AI can serve as a coherent enabling layer connecting operational safety and assurance processes, advancing prevention-oriented, continuously improving OSH systems. The study provides a foundation for future empirical research and policy development to ensure effective and accountable AI integration in occupational safety governance.
References
Abbasi, S. (2018). Defining safety hazards & risks in mining industry: a case-study in United States. Asian J. Appl. Sci. Technol.(AJAST), 2(2), 1071–1078. https://doi.org/10.1016/j.jsm.2018. 07.005
Ali, M. X. M., Arifin, K., Abas, A., Ahmad, M. A., Khairil, M., Cyio, M. B., Samad, M. A., Lampe, I., Mahfudz, M., & Ali, M. N. (2022). Systematic literature review on indicators use in safety management practices among utility industries. International Journal of Environmental Research and Public Health, 19(10), 6198. https://doi.org/10.3390/ijerph19106198
Almaskati, D., Kermanshachi, S., Pamidimukkala, A., Loganathan, K., & Yin, Z. (2024). A review on construction safety: hazards, mitigation strategies, and impacted sectors. Buildings, 14(2), 526. https://doi.org/10.3390/buildings14020526
Alqahtani, B. M., Alruqi, W., Bhandari, S., Abudayyeh, O., & Liu, H. (2022). The relationship between work-related stressors and construction workers’ self-reported injuries: a meta-analytic review. CivilEng, 3(4), 1091–1107. https://doi.org/10.3390/civileng3040062
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. ArXiv Preprint ArXiv:1606.06565, 29. https://doi.org/ 10.48550/arXiv. 1606.06565
Azizi, H., Aaleagha, M. M., Azadbakht, B., & Samadyar, H. (2022). Identification and Assessment of health, safety and environmental risk factors of Chemical Industry using Delphi and FMEA methods (a case study). Anthropogenic Pollution, 6(2). https://doi.org/ 10.22034/ ap.2022. 1971680.1138
Birhane, G. E., Yang, L., Geng, J., & Zhu, J. (2022). Causes of construction injuries: a review. International Journal of Occupational Safety and Ergonomics, 28(1), 343–353. https://doi.org/ 10.1080/10803548.2020.1761678
BIS:14489. (2018). Occupational Health and Safety Code of Practice: Vol. First Revi (Issue October, p. 28). Bureau of Indian Standards.
Campo, G., Cegolon, L., De Merich, D., Fedeli, U., Pellicci, M., Heymann, W. C., Pavanello, S., Guglielmi, A., & Mastrangelo, G. (2020). The Italian national surveillance system for occupational injuries: Conceptual framework and fatal outcomes, 2002–2016. International Journal of Environmental Research and Public Health, 17(20), 7631. https://doi.org/ 10.3390/ijerph17207631
Cinar, U., & Cebi, S. (2021). A novel approach to assess occupational risks and prevention of hazards: the house of safety & prevention. Journal of Intelligent & Fuzzy Systems, 42(1), 517–528. https://doi.org/10.3233/JIFS-219208
Colletaz, G., Hurlin, C., & Pérignon, C. (2013). The Risk Map: A new tool for validating risk models. Journal of Banking & Finance, 37(10), 3843–3854. https://doi.org/10.1016/j.jbankfin. 2013.06.006
da Silva, S. L. C., & Amaral, F. G. (2019). Critical factors of success and barriers to the implementation of occupational health and safety management systems: A systematic review of literature. Safety Science, 117, 123–132. https://doi.org/10.1016/j.ssci.2019.03.026
Debela, M. B., Azage, M., Begosaw, A. M., & Kabeta, N. D. (2022). Factors contributing to occupational injuries among workers in the construction, manufacturing, and mining industries in Africa: a systematic review and meta-analysis. Journal of Public Health Policy, 43(4), 487–502. https://doi.org/10.1057/s41271-022-00378-2
El-Helaly, M. (2024). Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks. La Medicina Del Lavoro, 115(2), e2024014. https://doi.org/10.23749/mdl.v115i2. 15835
Elumalai, V., Brindha, K., & Lakshmanan, E. (2017). Human exposure risk assessment due to heavy metals in groundwater by pollution index and multivariate statistical methods: a case study from South Africa. Water, 9(4), 234. https://doi.org/10.3390/w9040234
Erinjogunola, F. L., Sikhakhane-Nwokediegwu, Z., Ajirotutu, R. O., & Olayiwola, R. K. (2025). Enhancing bridge safety through AI-driven predictive analytics. International Journal of Social Science Exceptional Research. 2025, 4(2), 10–26. https://doi.org/10.54660/IJSSER.2025.4.2.10-26
Gil, M., Kozioł, P., Wróbel, K., & Montewka, J. (2022). Know your safety indicator–A determination of merchant vessels Bow Crossing Range based on big data analytics. Reliability Engineering & System Safety, 220, 108311. https://doi.org/10.1016/j.ress.2021.108311
Górny, A. (2020). Application of the MAC Method for Risk Assessment During Handling of Loads. In P. Golinska-Dawson, K.-M. Tsai, & M. Kosacka-Olejnik (Eds.), Smart and Sustainable Supply Chain and Logistics -- Trends, Challenges, Methods and Best Practices: Volume 1 (pp. 277–290). Springer International Publishing. https://doi.org/10.1007/978-3-030-61947-3_19
Gupta, T., & Roy, S. (2024). Applications of artificial intelligence in disaster management. Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence, 313–318. https: //doi.org/10.1145/3669754.36698
ILO. (2019). Safety and health at the heart of the future of work-Building on 100 years of experience. In ILO, Geneva (Issue April). www.ilo.org/labadmin-osh
ILO. (2023). Revolutionizing Health and Safety : The role of AI and digitalization at work. https://www. ilo.org/publications/revolutionizing-health-and-safety-role-ai-and-digitalization-work
Islam, M. I. (2025). AI-powered MIS for risk detection in industrial engineering projects. Authorea Preprints, 28. https://doi.org/10.36227/techrxiv.175825736.65590627/v1
ISO 45001. (2018). Occupational health and safety management systems — Requirements with guidance for use. International Organization for Standardization.
Jespersen, A. H., & Hasle, P. (2017). Developing a concept for external audits of psychosocial risks in certified occupational health and safety management systems. Safety Science, 99, 227–234. https://doi.org/10.1016/j.ssci.2016.11.023
Karadağ, T. (2024). Transformative role of artificial intelligence in enhancing occupational health and safety: A systematic review and meta-analysis. The European Research Journal, 11(3), 1–28. https://doi.org/10.18621/eurj.1561840
Karanikas, N., Weber, D., Bruschi, K., & Brown, S. (2022). Identification of systems thinking aspects in ISO 45001: 2018 on occupational health & safety management. Safety Science, 148, 105671. https://doi.org/10.1016/j.ssci.2022.105671
Khandan, M., Mosaferchi, S., & Koohpaei, A. (2017). Assessing exposure to risk factors for work-related musculoskeletal disorders using ART method in a manufacturing company. Archives of Hygiene Sciences Volume, 6(3), 259–267. https://doi.org/10.29252/ArchHygSci.6.3.259
Kim, K., Cho, Y., & Zhang, S. (2016). Integrating work sequences and temporary structures into safety planning: Automated scaffolding-related safety hazard identification and prevention in BIM. Automation in Construction, 70, 128–142. https://doi.org/10.1016/j.autcon.2016.06. 012
Kiral, I. A. (2025). Contextual Evaluation of Risk Identification Techniques for Construction Projects: Comparative Insights and a Decision-Support Model. Buildings, 15(20), 3806. https://doi.org /10.3390/buildings15203806
Kjellstrom, T., Holmer, I., & Lemke, B. (2009). Workplace heat stress, health and productivity–an increasing challenge for low and middle-income countries during climate change. Global Health Action, 2(1), 2047. https://doi.org/https://doi.org/10.3402/gha.v2i0.2047
Kyung, M., Lee, S.-J., Dancu, C., & Hong, O. (2023). Underreporting of workers’ injuries or illnesses and contributing factors: a systematic review. BMC Public Health, 23(1), 558. https://doi. org/10.1186/s12889-023-15487-0
Lee, J., Davari, H., SIngh, J., & Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23. https://doi.org/ 10.1016/ j.mfglet.2018.09.002
Lee, Y. C., Hariatfar, M., Rashidi, A., & Lee, H. W. (2020). Evidence-driven sound detection for prenotification and identification of construction safety hazards and accidents. Automation in Construction, 113, 103127. https://doi.org/10.1016/j.autcon.2020.103127
Li, X., Cheng, Y., Møller, C., & Lee, J. (2025). Data issues in industrial AI systems: A meta-review and research strategy. Computers in Industry, 173, 104361. https://doi.org/10.1016/j. compind.2025.104361
Marhavilas, P. K., Pliaki, F., & Koulouriotis, D. (2022). International management system standards related to occupational safety and health: An updated literature survey. Sustainability, 14(20), 13282. https://doi.org/10.3390/su142013282
Micheli, G. J. L., Farné, S., & Vitrano, G. (2022). A holistic view and evaluation of health and safety at work: enabling the assessment of the overall burden. Safety Science, 156, 105900. https: //doi.org/10.1016/j.ssci.2022.105900
Micheli, M., Ponti, M., Craglia, M., & Berti Suman, A. (2020). Emerging models of data governance in the age of datafication. Big Data & Society, 7(2), 2053951720948087. https://doi.org/ 10. 1177/2053951720948087
Nithilan, K., Kumar, M. V., Vishal, N., & Thangadurai, A. (2024). Advancing Workplace Safety with IoT-Enabled Industrial Monitoring. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 1–4. https://doi.org/10. 1109/ICRITO61523.2024.10522367
Noch, M. Y. (2024). A Critical Analysis of Risk Auditing: An Auditor’s Approach. Golden Ratio of Auditing Research, 4(1), 1–13. https://doi.org/https://doi.org/10.52970/grar.v4i1.383
Nunfam, V. F., Adusei-Asante, K., Van Etten, E. J., Oosthuizen, J., Adams, S., & Frimpong, K. (2019). The nexus between social impacts and adaptation strategies of workers to occupational heat stress: a conceptual framework. International Journal of Biometeorology, 63(12), 1693–1706. https://doi.org/10.1007/s00484-019-01775-1
Ozobu, C. O., Adikwu, F. E., Odujobi, N. O., Onyekwe, F. O., & Nwulu, E. O. (2025). Advancing occupational safety with AI-powered monitoring systems: A conceptual framework for hazard detection and exposure control. World Journal of Innovation and Modern Technology, 9(1), 186–213. https://doi.org/10.56201/wjimt.v9.no1.2025.pg186.213
Park, J., Kim, K., & Cho, Y. K. (2017). Framework of automated construction-safety monitoring using cloud-enabled BIM and BLE mobile tracking sensors. Journal of Construction Engineering and Management, 143(2), 5016019. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001223
Podgórski, D. (2015). Measuring operational performance of OSH management system–A demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146–166. https://doi.org/10.1016/j.ssci.2014.11.018
PRYTULSKA, N., ANTIUSHKO, D., & GUSAREVICH, N. (2019). International standard ISO 19011: 2018: perspectives of implementation. INTERNATIONAL SCIENTIFIC-PRACTICAL JOURNAL COMMODITIES AND MARKETS, 32(4), 5–15. https://doi.org/10.31617/tr. knute.2019(32)01
Quaigrain, R. A., & Issa, M. H. (2023). Comparative analysis of leading and lagging indicators of construction disability management performance: an exploratory study. International Journal of Construction Management, 23(7), 1205–1213. https://doi.org/10.1080/15623599.2021. 1963921
Ravi, P., & Janarthanan, S. (2024). Machine learning models for intelligent hazard management. In AI for Climate Change and Environmental Sustainability (pp. 88–97). CRC Press. https://doi.org /10.1201/9781003452393
Rose, L. M., Eklund, J., Nord Nilsson, L., Barman, L., & Lind, C. M. (2020). The RAMP package for MSD risk management in manual handling – A freely accessible tool, with website and training courses. Applied Ergonomics, 86, 103101. https://doi.org/10.1016/j.apergo.2020. 103101
Rossi, D., Bertoloni, E., Fenaroli, M., Marciano, F., & Alberti, M. (2013). Analytic hierarchy process to support the safety and ergonomic assessment of alternatives in “manuable” material handling. IFAC Proceedings Volumes, 46(9), 525–530. https://doi.org/10.3182/20130619-3-RU-3018.00305
Sadeghi, H., Mohandes, S. R., Yunusa-Kaltungo, A., Cheung, C., & Manu, P. (2025). A state-of-the-art review of safety leading indicators across diverse industries. Journal of Safety Science and Resilience, 100272. https://doi.org/10.1016/j.jnlssr.2025.100272
Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS One, 10(3), e0118432. https://doi.org/10.1371/journal.pone.0118432
Saxena, V. (2024). Predictive analytics in occupational health and safety. ArXiv Preprint ArXiv:2412. 16038. https://doi.org/10.48550/arXiv.2412.16038
Skeja, A., & Sadiku-Dushi, N. (2025). Toward sustainable AI leadership: ethical blind spots, accountability gaps and the CARE governance framework. Leadership & Organization Development Journal, 1–19. https://doi.org/https://doi.org/10.1108/LODJ-06-2025-0530
Tian, K., Zhu, Z., Mbachu, J., Ghanbaripour, A., & Moorhead, M. (2025). Artificial intelligence in risk management within the realm of construction projects: A bibliometric analysis and systematic literature review. Journal of Innovation & Knowledge, 10(3), 100711. https://doi.org /10.1016/j.jik.2025.100711
Tixier, A. J.-P., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2016). Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports. Automation in Construction, 62, 45–56. https:// doi.org/10.1016/j.autcon.2015.11.001
Usama, M., Ullah, U., Muhammad, Z., Islam, T., & saba Hashmi, S. (2024). AI-enabled risk assessment and safety management in construction. In Ethical Artificial Intelligence in Power Electronics (pp. 105–132). CRC Press. https://doi.org/https://doi.org/10.1201/ 9781032648323
Usul, H., & Alpay, B. Y. (2025). Digital Transformation in Internal Audit: Paradigm Shifts, Emerging Risks, and Strategic Resilience. European Journal of Digital Economy Research, 6(1), 23–36. https://doi.org/10.5281/zenodo.15660150
Waters, T. R., Putz-Anderson, V., Garg, A., & Fine, L. J. (1993). Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics, 36(7), 749–776. https://doi.org /10.1080/00140139308967940
Yorio, P. L., Haas, E. J., Bell, J. L., Moore, S. M., & Greenawald, L. A. (2020). Lagging or leading? Exploring the temporal relationship among lagging indicators in mining establishments 2006–2017. Journal of Safety Research, 74, 179–185. https://doi.org/10.1016/j.jsr.2020.06.018
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