This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

1. Abbas, Z., Anjum, M. R., Younus, M. U., et al. (2021). Monitoring of Gas Distribution Pipelines Network Using Wireless Sensor Networks. Wireless Personal Communications, 117(3), 2575–2594. https://doi.org/10.1007/s11277-020-07997-6
2. Abioye, S. O., Oyedele, L. O., Akanbi, L., et al. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299. https://doi.org/10.1016/j.jobe.2021.103299
3. Adem, A., Çakit, E., & Dağdeviren, M. (2020). Occupational health and safety risk assessment in the domain of Industry 4.0. SN Applied Sciences, 2(5). https://doi.org/10.1007/s42452-020-2817-x
4. Ajunwa, I., & Greene, D. (2019). Chapter 3 Platforms at Work: Automated Hiring Platforms and Other New Intermediaries in the Organization of Work. Research in the Sociology of Work, 61–91. https://doi.org/10.1108/s0277-283320190000033005
5. Akinsemoyin, A., Awolusi, I., Chakraborty, D., et al. (2023). Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites. Sensors, 23(15), 6690. https://doi.org/10.3390/s23156690
6. Ali, D., & Frimpong, S. (2020). Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector. Artificial Intelligence Review, 53(8), 6025–6042. https://doi.org/10.1007/s10462-020-09841-6
7. Arzhaeva, Y., Wang, D., Devnath, L., et al. (2019). Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-Ray Radiographs. Available online: https://www.coalservices.com.au/wp-content/uploads/2023/06/Project-No.-20647-Final-Report.pdf (accessed on 2 March 2024)
8. Aslan, I., Benharkat, N. E. H., & Bentaalla-Kaced, S. (2023). The Applications of Artificial Intelligence in Occupational Health and Safety. In: Cesser 7th international conference on emprical social sciences, economy, management and education researches; Jakarta–Indonesia.
9. Baek, J., Choi, Y., Lee, C., et al. (2017). BBUNS: Bluetooth Beacon-Based Underground Navigation System to Support Mine Haulage Operations. Minerals, 7(11), 228. https://doi.org/10.3390/min7110228
10. Bhagwat, A., Srinivasan, R., & Krishnaswamy, P. R. (2003a). Fault detection during process transitions: A model-based approach. Chemical Engineering Science, 58, 309-325. https://doi.org/10.1016/S0009-2509(02)00520-1
11. Bhagwat, A., Srinivasan, R., & Krishnaswamy, P. R. (2003b). Multi-linear model-based fault detection during process transitions. Chemical Engineering Science, 58(9), 1649-1670. https://doi.org/https://doi.org/10.1016/S0009-2509(03)00008-3
12. Bhana, R., Mahmoud, H., & Idrissi, M. (2023). Smart Industrial Safety using Computer Vision. 2023 28th International Conference on Automation and Computing (ICAC). https://doi.org/10.1109/icac57885.2023.10275164
13. Bieri, J. S., Ikae, C., Souissi, S. B., et al. (2024). Natural Language Processing for Work-Related Stress Detection Among Health Professionals: Protocol for a Scoping Review. JMIR Research Protocols, 13, e56267. https://doi.org/10.2196/56267
14. Blackley, D. J., Halldin, C. N., & Laney, A. S. (2018). Continued Increase in Prevalence of Coal Workers’ Pneumoconiosis in the United States, 1970–2017. American Journal of Public Health, 108(9), 1220–1222. https://doi.org/10.2105/ajph.2018.304517
15. Boadu, E. F., Okeke, S. R., Boadi, C., et al. (2023). Work-related respiratory health conditions among construction workers: a systematic narrative review. BMJ Open Respiratory Research, 10(1), e001736. https://doi.org/10.1136/bmjresp-2023-001736
16. Bonato, P. (2005). Advances in wearable technology and applications in physical medicine and rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2(1). https://doi.org/10.1186/1743-0003-2-2
17. Boschman, J. S., van der Molen, H. F., Sluiter, J. K., et al. (2012). Musculoskeletal disorders among construction workers: a one-year follow-up study. BMC Musculoskeletal Disorders, 13(1). https://doi.org/10.1186/1471-2474-13-196
18. Bozinovski, S. (2020). Reminder of the First Paper on Transfer Learning in Neural Networks, 1976. Informatica, 44(3). https://doi.org/10.31449/inf.v44i3.2828
19. Çalış, S., & Büyükakıncı, B. Y. (2019). Occupational Health and Safety Management Systems Applications and A System Planning Model. Procedia Computer Science, 158, 1058–1066. https://doi.org/10.1016/j.procs.2019.09.147
20. Çallı, E., Sogancioglu, E., van Ginneken, B., et al. (2021). Deep learning for chest X-ray analysis: A survey. Medical Image Analysis, 72, 102125. https://doi.org/10.1016/j.media.2021.102125
21. Campero-Jurado, I., Márquez-Sánchez, S., Quintanar-Gómez, J., et al. (2020). Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence. Sensors, 20(21), 6241. https://doi.org/10.3390/s20216241
22. Cellina, M., Cè, M., Irmici, G., et al. (2022). Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics, 12(11), 2644. https://doi.org/10.3390/diagnostics12112644
23. Choe, J., Lee, S. M., Hwang, H. J., et al. (2022). Artificial Intelligence in Lung Imaging. Seminars in Respiratory and Critical Care Medicine, 43(06), 946–960. CLOCKSS. https://doi.org/10.1055/s-0042-1755571
24. Chraim, F., Bugra Erol, Y., & Pister, K. (2016). Wireless Gas Leak Detection and Localization. IEEE Transactions on Industrial Informatics, 12(2), 768–779. https://doi.org/10.1109/tii.2015.2397879
25. Desikan, J., & Devi, A. J. (2023). AI and ML-based Assessment to Reduce Risk in Oil and Gas Retail Filling Station: A Literature Review. Journal of Information Technology and Digital World, 4(4), 299–316. https://doi.org/10.36548/jitdw.2022.4.005
26. Devnath, L., Summons, P., Luo, S. et al. (2022). Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review. In International Journal of Environmental Research and Public Health ,19(11). https://doi.org/10.3390/ijerph19116439
27. Dey, P., Chaulya, S. K., & Kumar, S. (2021). Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system. Process Safety and Environmental Protection, 152, 249–263. https://doi.org/10.1016/j.psep.2021.06.005
28. Dey, P., Kumar, C., Mitra, M., et al. (2021). Deep convolutional neural network based secure wireless voice communication for underground mines. Journal of Ambient Intelligence and Humanized Computing, 12(10), 9591–9610. https://doi.org/10.1007/s12652-020-02700-w
29. Dogan, G., Arslan, M. H., & Ceylan, M. (2017). Concrete compressive strength detection using image processing based new test method. Measurement, 109, 137–148. https://doi.org/10.1016/j.measurement.2017.05.051
30. DOSH. (2023). DOSH Occupentional Research Statestics 2023. Available online: https://www.dosh.gov.my/index.p%0Ahp/list-of%02documents/statistics/occupational%02accident-statistics/occupational-accident%02statistic-2023(accessed on 2 March 2024).
31. Fang, Y., Cho, Y., Zhang, S., & Perez, E. (2016). Case Study of BIM and Cloud-Enabled Real-Time RFID Indoor Localization for Construction Management Applications. Journal of Construction Engineering and Management, 142, 5016003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001125
32. Gao, Y., González, V. A., Yiu, T. W., et al. (2021). Immersive virtual reality as an empirical research tool: exploring the capability of a machine learning model for predicting construction workers’ safety behaviour. Virtual Reality, 26(1), 361–383. https://doi.org/10.1007/s10055-021-00572-9
33. Garcia Fracaro, S., Chan, P., Gallagher, T., et al. (2021). Towards design guidelines for virtual reality training for the chemical industry. Education for Chemical Engineers, 36, 12–23. https://doi.org/10.1016/j.ece.2021.01.014
34. Gonzalez, C., Burguillo, J. C., & Llamas, M. (2007). Integrating Intelligent Tutoring Systems and Health Information Systems. 18th International Conference on Database and Expert Systems Applications (DEXA 2007). https://doi.org/10.1109/dexa.2007.57
35. Gül, M., & Ak, M. F. (2018). A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. Journal of Cleaner Production, 196, 653-664. https://doi.org/10.1016/j.jclepro.2018.06.106
36. Gunduz, M., & Laitinen, H. (2018). Construction safety risk assessment with introduced control levels. Journal of Civil Engineering and Management, 24(1), 11–18. https://doi.org/10.3846/jcem.2018.284
37. Guo, H., Yu, Y., & Skitmore, M. (2017). Visualization technology-based construction safety management: A review. Automation in Construction, 73, 135-144. https://doi.org/https://doi.org/10.1016/j.autcon.2016.10.004
38. Guo, S. Y., Ding, L. Y., Luo, H. B., et al. (2016). A Big-Data-based platform of workers’ behavior: Observations from the field. Accident Analysis & Prevention, 93, 299–309. https://doi.org/10.1016/j.aap.2015.09.024
39. Ham, Y., Han, K. K., Lin, J. J., et al. (2016). Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works. Visualization in Engineering, 4(1). https://doi.org/10.1186/s40327-015-0029-z
40. Hammoudeh, M. A. A., Alsaykhan, M., Alsalameh, R., et al. (2022). Computer Vision: A Review of Detecting Objects in Videos –Challenges and Techniques. International Journal of Online and Biomedical Engineering (IJOE), 18(01), 15–27. https://doi.org/10.3991/ijoe.v18i01.27577
41. Hejmanowski, R., & Witkowski, W. T. (2015). Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage. Journal of Sustainable Mining, 14(2), 101–107. https://doi.org/10.1016/j.jsm.2015.08.014
42. Hurtado, G., Rojas, R., Mauricio, D. M., et al. (2022). Expert System for the Prevention of Occupational Risks in Construction - Residential Buildings. TEM Journal, 1748–1757. Portico. https://doi.org/10.18421/tem114-41
43. Hyder, Z., Siau, K., & Nah, F. (2019). Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry. Journal of Database Management, 30(2), 67–79. https://doi.org/10.4018/jdm.2019040104
44. Iannizzotto, G., Lo Bello, L., & Patti, G. (2021). Personal Protection Equipment detection system for embedded devices based on DNN and Fuzzy Logic. Expert Systems with Applications, 184, 115447. https://doi.org/10.1016/j.eswa.2021.115447
45. Imam, M., Baïna, K., Tabii, Y., et al. (2023). The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines. Sensors, 23(9), 4294. https://doi.org/10.3390/s23094294
46. International Labour Organization. (2011). The enormous burden of poor working conditions. Available online: https://www.ilo.org/moscow/area%0As-of-work/occupational-safety-and%02health/WCMS_249278/lang--en/index.htm (accessed on 2 March 2024)
47. Jagoe, J. R. (1979). Gradient pattern coding-An application to the measurement of pneumoconiosis in chest X rays. Computers and Biomedical Research, an International Journal, 12(1), 1-15. https://doi.org/10.1016/0010-4809(79)90002-8
48. James, S. L., Abate, D., Abate, K. H., et al. (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 1789-1858. https://doi.org/10.1016/S0140-6736(18)32279-7
49. Jarota, M. (2023). Artificial intelligence in the work process. A reflection on the proposed European Union regulations on artificial intelligence from an occupational health and safety perspective. Computer Law & Security Review, 49, 105825. https://doi.org/10.1016/j.clsr.2023.105825
50. Jo, B. W., & Khan, R. M. A. (2017). An event reporting and early-warning safety system based on the internet of things for underground coal mines: A case study. Applied Sciences (Switzerland), 7(9). https://doi.org/10.3390/app7090925
51. Kas, K. A., & Johnson, G. K. (2020). Using unmanned aerial vehicles and robotics in hazardous locations safely. Process Safety Progress, 39(1), e12066. https://doi.org/https://doi.org/10.1002/prs.12066
52. Katsuragawa, S., Doi, K., MacMahon, H., et al. (1990). Quantitative computer-aided analysis of lung texture in chest radiographs. RadioGraphics, 10(2), 257–269. https://doi.org/10.1148/radiographics.10.2.2326513
53. Kim, H., & Choi, Y. (2021). Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs. Applied Sciences, 11(21), 10235. https://doi.org/10.3390/app112110235
54. 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
55. Kim, Kinam, Kim, H., & Kim, H. (2017). Image-based construction hazard avoidance system using augmented reality in wearable device. Automation in Construction, 83. https://doi.org/10.1016/j.autcon.2017.06.014
56. Kondo, H., & Kouda, T. (2001). Computer-aided diagnosis for pneumoconiosis using neural network. Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001. https://doi.org/10.1109/cbms.2001.941763
57. Kondo, Hiroshi, Zhang, L., & Koda, T. (2000). Computer aided diagnosis for pneumoconiosis. International Archives of Photogrammetry and Remote Sensing, XXXIII, 453-458.
58. Kontogiannis, T., Leva, M. C., & Balfe, N. (2017). Total Safety Management: Principles, processes and methods. Safety Science, 100, 128–142. https://doi.org/10.1016/j.ssci.2016.09.015
59. Kruger, R. P., Thompson, W. B., & Turner, A. F. (1974). Computer Diagnosis of Pneumoconiosis. IEEE Transactions on Systems, Man, and Cybernetics, SMC-4(1), 40–49. https://doi.org/10.1109/tsmc.1974.5408519
60. Kuhar, P., Sharma, K., Hooda, Y., et al. (2021). Internet of Things (IoT) based Smart Helmet for Construction. Journal of Physics: Conference Series, 1950(1), 012075. https://doi.org/10.1088/1742-6596/1950/1/012075
61. Kumar, V. V., Carberry, D., Beenfeldt, C., et al. (2021). Virtual reality in chemical and biochemical engineering education and training. Education for Chemical Engineers, 36, 143–153. https://doi.org/10.1016/j.ece.2021.05.002
62. Kumari, S., Chakravarty, D., Pulakesh, D., & Bandyopadhyay, J. (2014). Hyperion Image Analysis for Iron Ore Mapping in Gua Iron Ore Region, Jharkhand, India. International Research Journal of Earth Sciences, 2, 2321-2527.
63. Kuok Ho, D. T., Dawal, S., & Olugu, E. (2018). Generating Safety Performance Scores of Offshore Oil and Gas Platforms in Malaysia. In: One Curtin International Postgraduate Conference (OCPC) 2018, Curtin University Malaysia, Miri, Sarawak, Malaysia
64. Lal, B., & Tripathy, S. S. (2012). Prediction of dust concentration in open cast coal mine using artificial neural network. Atmospheric Pollution Research, 3(2), 211–218. https://doi.org/10.5094/apr.2012.023
65. Lee, P., Kim, H., Zitouni, M. S., et al. (2022). Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review. JMIR MHealth and UHealth, 10(11), e40797. https://doi.org/10.2196/40797
66. Li, B., Hou, B., Yu, W., et al. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86–96. https://doi.org/10.1631/fitee.1601885
67. Li, H., Yang, X., Wang, F., et al. (2016). Stochastic state sequence model to predict construction site safety states through Real-Time Location Systems. Safety Science, 84, 78–87. https://doi.org/10.1016/j.ssci.2015.11.025
68. Li, J., Yin, P., Wang, H., et al. (2022). The burden of pneumoconiosis in China: an analysis from the Global Burden of Disease Study 2019. BMC Public Health, 22(1). https://doi.org/10.1186/s12889-022-13541-x
69. Li, M., Sun, Z., Jiang, Z., et al. (2020). A Virtual Reality Platform for Safety Training in Coal Mines with AI and Cloud Computing. Discrete Dynamics in Nature and Society, 2020, 1–7. https://doi.org/10.1155/2020/6243085
70. Li, P., Wu, F., Xue, S., & Guo, L. (2023). Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO. Sensors (Basel, Switzerland), 23(14). https://doi.org/10.3390/s23146318
71. Li, X., Yi, W., Chi, H.-L., et al. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86, 150-162. https://doi.org/https://doi.org/10.1016/j.autcon.2017.11.003
72. Lisi, M. P., Scattolin, M., Fusaro, M., et al. (2021). A Bayesian approach to reveal the key role of mask wearing in modulating projected interpersonal distance during the first COVID-19 outbreak. PLOS ONE, 16(8), e0255598. https://doi.org/10.1371/journal.pone.0255598
73. Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
74. Liu, P., Huang, Z., Duan, S., et al. (2015). Optimization for Remote Monitoring Terrestrial Petroleum Pipeline Cathode Protection System Using Graded Network. International Journal of Smart Home, 9(6), 51–64. https://doi.org/10.14257/ijsh.2015.9.6.06
75. Liu, Peter, Chen, A., et al. (2014). A review of rotorcraft Unmanned Aerial Vehicle (UAV) developments and applications in civil engineering. Smart structures and systems, 13, 1065-1094. https://doi.org/10.12989/sss.2014.13.6.1065
76. Loey, M., Manogaran, G., Taha, M. H. N., et al. (2021). A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167, 108288. https://doi.org/10.1016/j.measurement.2020.108288
77. Lozano, R., Naghavi, M., Foreman, K., et al. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2095-2128. https://doi.org/10.1016/S0140-6736(12)61728-0
78. Márquez-Sánchez, S., Campero-Jurado, I., Herrera-Santos, J., et al. (2021). Intelligent Platform Based on Smart PPE for Safety in Workplaces. Sensors, 21(14), 4652. https://doi.org/10.3390/s21144652
79. Mazlan, A. N., Yahya, K., Haron, Z., et al. (2018). Characteristic of Noise-induced Hearing Loss among Workers in Construction Industries. E3S Web of Conferences, 34, 02025. https://doi.org/10.1051/e3sconf/20183402025
80. Mistikoglu, G., Gerek, I. H., Erdis, E., et al. (2015). Decision tree analysis of construction fall accidents involving roofers. Expert Systems with Applications, 42(4), 2256–2263. https://doi.org/10.1016/j.eswa.2014.10.009
81. Mitchell, O. (2019). Robots can play key roles in repairing our infrastructure. Available online: https://robohub.org/robots-can-play-key-roles-in-repairing-our-infrastructure/ (accessd on 2 March 2024)
82. Mohammadfam, I., Ghasemi, F., Kalatpour, O., et al. (2017). Constructing a Bayesian network model for improving safety behavior of employees at workplaces. Applied Ergonomics, 58, 35–47. https://doi.org/10.1016/j.apergo.2016.05.006
83. Moore, P. V. (2019). OSH and the Future of Work: Benefits and Risks of Artificial Intelligence Tools in Workplaces BT - Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Body and Motion (V. G. Duffy, ed.). Cham: Springer International Publishing.
84. Morris, D., & Joppa, L. (2021). Challenges for the computer vision community. Conservation Technology, 225–238. https://doi.org/10.1093/oso/9780198850243.003.0011
85. Murugesan, U., Subramanian, P., Srivastava, S., et al. (2023). A study of Artificial Intelligence impacts on Human Resource Digitalization in Industry 4.0. Decision Analytics Journal, 7, 100249. https://doi.org/10.1016/j.dajour.2023.100249
86. Nath, N. D., Behzadan, A. H., & Paal, S. G. (2020). Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction, 112, 103085. https://doi.org/10.1016/j.autcon.2020.103085
87. Nayak, S., & Kumar Das, R. (2020). Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation. Service Robotics. https://doi.org/10.5772/intechopen.93903
88. Nguyen, L., Tran, D., & Chandrawinata, M. (2016). Predicting Safety Risk of Working at Heights Using Bayesian Networks. Journal of Construction Engineering and Management, 142, 4016041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001154
89. Nooralishahi, P., Ibarra-Castanedo, C., Deane, S., et al. (2021). Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies. Drones, 5(4), 106. https://doi.org/10.3390/drones5040106
90. Okumura, E., Kawashita, I., & Ishida, T. (2014). Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods. Radiological Physics and Technology, 7(2), 217–227. https://doi.org/10.1007/s12194-013-0255-9
91. Okumura, E., Kawashita, I., & Ishida, T. (2017). Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages. Journal of Digital Imaging, 30(4), 413–426. https://doi.org/10.1007/s10278-017-9942-0
92. Osunmakinde, I. O. (2013). Towards Safety from Toxic Gases in Underground Mines Using Wireless Sensor Networks and Ambient Intelligence. International Journal of Distributed Sensor Networks, 9(2), 159273. https://doi.org/10.1155/2013/159273
93. Oyedele, A., Ajayi, A., Oyedele, L. O., et al. (2021). Deep learning and Boosted trees for injuries prediction in power infrastructure projects. Applied Soft Computing, 110, 107587. https://doi.org/10.1016/j.asoc.2021.107587
94. Palaniappan, K., Lin, E. Y. T., & Vogel, S. (2024). Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector. Healthcare, 12(5), 562. https://doi.org/10.3390/healthcare12050562
95. Panja, S., Chattopadhyay, A. K., Nag, A., et al. (2023). Fuzzy-logic-based IoMT framework for COVID19 patient monitoring. Computers & Industrial Engineering, 176, 108941. https://doi.org/10.1016/j.cie.2022.108941
96. Park, J., Kim, K., & Cho, Y. (2016). Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and BLE Mobile Tracking Sensors. Journal of Construction Engineering and Management, 143, 5016019. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001223
97. Park, M.-W., & Brilakis, I. (2015). Improved Localization of Construction Workers in Video Frames by Integrating Detection and Tracking. In: Proceedings of the 15th CSCE International Construction Specialty Conference.
98. Podgórski, D., Majchrzycka, K., Dąbrowska, A., et al. (2016). Towards a conceptual framework of OSH risk management in smart working environments based on smart PPE, ambient intelligence and the Internet of Things technologies. International Journal of Occupational Safety and Ergonomics, 23(1), 1–20. https://doi.org/10.1080/10803548.2016.1214431
99. Qi, X.-M., Luo, Y., Song, M.-Y., et al. (2021). Pneumoconiosis: current status and future prospects. Chinese Medical Journal, 134(8), 898–907. https://doi.org/10.1097/cm9.0000000000001461
100. Rafie, M., & Samimi Namin, F. (2015). Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system. International Journal of Mining Science and Technology, 25(4), 655–663. https://doi.org/10.1016/j.ijmst.2015.05.021
101. Rashid, S., Akram, U., & Khan, S. A. (2015). WML: Wireless Sensor Network based Machine Learning for Leakage Detection and Size Estimation. Procedia Computer Science, 63, 171–176. https://doi.org/10.1016/j.procs.2015.08.329
102. Reddy, GopireddyM. M., Nisha, B., Prabhushankar, T., et al. (2016). Musculoskeletal morbidity among construction workers: A cross-sectional community-based study. Indian Journal of Occupational and Environmental Medicine, 20(3), 144. https://doi.org/10.4103/0019-5278.203134
103. Rokooei, S., Shojaei, A., Alvanchi, A., et al. (2023). Virtual reality application for construction safety training. Safety Science, 157, 105925. https://doi.org/10.1016/j.ssci.2022.105925
104. Rooki, R., Doulati Ardejani, F., & Moradzadeh, A. (2014). Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression. Geomaterials, 04(01), 47–53. https://doi.org/10.4236/gm.2014.41005
105. Sacks, R., Perlman, A., & Barak, R. (2013). Construction safety training using immersive virtual reality. Construction Management and Economics, 31(9), 1005–1017. https://doi.org/10.1080/01446193.2013.828844
106. Sanchez, M., Sergio, Rodriguez, C., & Manuel, J. (2020). Smart Protective Protection Equipment for an accessible work environment and occupational hazard prevention. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). https://doi.org/10.1109/confluence47617.2020.9058188
107. Scorgie, D., Feng, Z., Paes, D., et al. (2024). Virtual reality for safety training: A systematic literature review and meta-analysis. Safety Science, 171, 106372. https://doi.org/10.1016/j.ssci.2023.106372
108. Shadizadeh, S. R., Karimi, F., & Zoveidavianpoor, M. (2010). Drilling Stuck Pipe Prediction in Iranian Oil Fields: An Artificial Neural Network Approach. Iranian Journal of Chemical Engineering, 7.
109. Shah, I. A., & Mishra, S. (2024). Artificial intelligence in advancing occupational health and safety: an encapsulation of developments. Journal of Occupational Health, 66(1). https://doi.org/10.1093/joccuh/uiad017
110. Shahmoradi, J., Talebi, E., Roghanchi, P., et al. (2020). A Comprehensive Review of Applications of Drone Technology in the Mining Industry. Drones, 4(3), 34. https://doi.org/10.3390/drones4030034
111. Sharma, M., Suri, N. M., & Kant, S. (2022). Analyzing occupational heat stress using sensor-based monitoring: a wearable approach with environmental ergonomics perspective. International Journal of Environmental Science and Technology, 19(11), 11421–11434. https://doi.org/10.1007/s13762-021-03862-6
112. Sishodiya, P. K. (2022). Silicosis-An Ancient Disease: Providing Succour to Silicosis Victims, Lessons from Rajasthan Model. In Indian journal of occupational and environmental medicine , 26(2). https://doi.org/10.4103/ijoem.ijoem_160_22
113. Su, Y., Yang, S., Liu, K., et al. (2019). Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry. International Journal of Environmental Research and Public Health, 16(9), 1511. https://doi.org/10.3390/ijerph16091511
114. Sun, K., & Azman, A. S. (2018). Evaluating hearing loss risks in the mining industry through MSHA citations. Journal of Occupational and Environmental Hygiene, 15(3), 246–262. https://doi.org/10.1080/15459624.2017.1412584
115. Sun, S., Zheng, X., Gong, B., et al. (2020). Healthy Operator 4.0: A Human Cyber–Physical System Architecture for Smart Workplaces. Sensors, 20(7), 2011. https://doi.org/10.3390/s20072011
116. Szrek, J., Zimroz, R., Wodecki, J., et al. (2020). Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine—The AMICOS Project. Remote Sensing, 13(1), 69. https://doi.org/10.3390/rs13010069
117. Tang, K. H. D. (2024). Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects. Journal of Engineering Research and Reports, 26(6), 241–253. https://doi.org/10.9734/jerr/2024/v26i61177
118. Tang, K. H. D., Md Dawal, S. Z., & Olugu, E. U. (2018). Integrating fuzzy expert system and scoring system for safety performance evaluation of offshore oil and gas platforms in Malaysia. Journal of Loss Prevention in the Process Industries, 56, 32–45. https://doi.org/10.1016/j.jlp.2018.08.005
119. Tetik, Y. O., Kale, O. A., Bayram, I., et al. (2021). Applying decision tree algorithm to explore occupational injuries in the Turkish construction industry. Journal of Engineering Research. https://doi.org/10.36909/jer.12209
120. Tyrrell, P. (2021). 9 Construction Accident & Safety Statistics in 2021. Available online: https://www.fieldinsight.com/blog/construction-accident-statistics/ (accessed on 2 March 2024).
121. Uhrenholdt Madsen, C., Kirkegaard, M. L., Dyreborg, J., et al. (2020). Making occupational health and safety management systems ‘work’: A realist review of the OHSAS 18001 standard. Safety Science, 129, 104843. https://doi.org/10.1016/j.ssci.2020.104843
122. UNDRR. (2024). Safety Hazard Associated with Oil and Gas. Available online: https://www.undrr.org/understand%0Aing-disaster-risk/terminology/hips/tl0034. (accessed on 2 March 2024).
123. van Wyk, E., & de Villiers, R. (2009). Virtual reality training applications for the mining industry. Proceedings of the 6th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa. https://doi.org/10.1145/1503454.1503465
124. Vlahovich, K. P., & Sood, A. (2020). A 2019 Update on Occupational Lung Diseases: A Narrative Review. Pulmonary Therapy, 7(1), 75–87. https://doi.org/10.1007/s41030-020-00143-4
125. Vos, T., Barber, R., Bell, B., et al.(2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 386, 743-800. https://doi.org/10.1016/S0140-6736(15)60692-4
126. Vukicevic, A. M., Djapan, M., Isailovic, V., et al. (2022). Generic compliance of industrial PPE by using deep learning techniques. Safety Science, 148, 105646. https://doi.org/10.1016/j.ssci.2021.105646
127. Wang, X., Yu, J., Zhu, Q., et al. (2020). Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography. Occupational and Environmental Medicine, 77(9), 597–602. https://doi.org/10.1136/oemed-2019-106386
128. Wang, Y., Cui, F., Ding, X., et al. (2023). Automated identification of the preclinical stage of coal workers’ pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model. Biomedical Signal Processing and Control, 83, 104607. https://doi.org/10.1016/j.bspc.2023.104607
129. Wu, F., Liu, W., Wang, S., et al. (2023). Improved mine pedestrian detection algorithm based on YOLOv4-Tiny.In: Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022). https://doi.org/10.1117/12.2661076
130. Wu, W., & Chen, Y. (2022). A Trustworthy Classification Model for Intelligent Building Fire Risk. IEEE Access, 10, 10371–10383. https://doi.org/10.1109/access.2022.3143614
131. Xiao, L., Fei, L. C., Li, G., et al. (2021). Deep Learning in Chest Radiography: Detection of Pneumoconiosis. Biomedical and Environmental Sciences : BES, 34(10), 842-845.
132. Xue, Y., Cai, X., Shadabfar, M., et al. (2020). Deep learning-based automatic recognition of water leakage area in shield tunnel lining. Tunnelling and Underground Space Technology, 104, 103524. https://doi.org/https://doi.org/10.1016/j.tust.2020.103524
133. Yedla, A., Kakhki, F. D., & Jannesari, A. (2020). Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations. International Journal of Environmental Research and Public Health, 17(19), 7054. https://doi.org/10.3390/ijerph17197054
134. Zhang, H. (2017). Head-mounted display-based intuitive virtual reality training system for the mining industry. International Journal of Mining Science and Technology, 27(4), 717–722. https://doi.org/10.1016/j.ijmst.2017.05.005
135. Zhang, L., Rong, R., Li, Q., et al. (2021). A deep learning-based model for screening and staging pneumoconiosis. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-020-77924-z
136. Zhang, S., Sulankivi, K., Kiviniemi, M., et al. (2015). BIM-based fall hazard identification and prevention in construction safety planning. Safety Science, 72, 31–45. https://doi.org/10.1016/j.ssci.2014.08.001
137. Zhang, Y. (2021). Safety Management of Civil Engineering Construction Based on Artificial Intelligence and Machine Vision Technology. Advances in Civil Engineering, 2021, 1–14. https://doi.org/10.1155/2021/3769634
138. Zhou, S., & Gheisari, M. (2018). Unmanned aerial system applications in construction: a systematic review. Construction Innovation, 18(4), 453–468. https://doi.org/10.1108/ci-02-2018-0010