Do occupational health and safety tools that utilize artificial intelligence have a measurable impact on worker injury or illness? Findings from a systematic review

Howard J. Artificial intelligence: implications for the future of work. Am J Ind Med. 2019;62(11):917–26.

PubMed  Google Scholar 

Jetha A, Bakhtari H, Rosella LC, Gignac MAM, Biswas A, Shahidi FV, et al. Artificial intelligence and the work–health interface: a research agenda for a technologically transforming world of work. Am J Ind Med. 2023;66(10):815–30.

PubMed  Google Scholar 

Brynjolfsson E, Mitchell T. What can machine learning do? Workforce implications Science. 2017;358(6370):1530–4.

CAS  PubMed  Google Scholar 

Agrawal A, Gans J, Goldfarb A. Prediction machines: the simple economics of artificial intelligence. Harvard Way, BA: Harvard Business Press; 2018.

Google Scholar 

Webb M. The impact of artificial intelligence on the labor market. SSRN. 2019:61:1-60.

Khairuddin MZF, Hasikin K, Abd Razak NA, Lai KW, Osman MZ, Aslan MF, et al. Predicting occupational injury causal factors using text-based analytics: a systematic review. Front Public Health. 2022;10:84099.

Maheronnaghsha S, Zolfagharnasabb H, Gorgichc M, Duarte J. Machine learning in occupational safety and health–a systematic review. Int J Occup Environ Safety. 2023;7(1):14–32.

Sarkar S, Vinay S, Raj R, Maiti J, Mitra P. Application of optimized machine learning techniques for prediction of occupational accidents. Comput Oper Res. 2019;106:210–24.

Google Scholar 

Tixier AJP, Hallowell MR, Rajagopalan B, Bowman D. Application of machine learning to construction injury prediction. Autom Constr. 2016;69:102–14.

Google Scholar 

Mustard C, Tompa E, Landsman V, Lay M. What do employers spend to protect the health of workers? Scand J Work Environ Health. 2019;3:308–11.

Google Scholar 

International Labour Organization. Safety + health for all - key facts and figures (2016- 2020). 2020.

Chambers A, Ibrahim S, Etches J, Mustard C. Diverging trends in the incidence of occupational and nonoccupational injury in Ontario, 2004–2011. Am J Public Health. 2014;105(2):338–43.

Google Scholar 

Tucker S, Keefe A. 2022 Report on work fatality and injury rates in Canada. 2022.

Workplace safety and insurance board. Health and Safety Statistics. 2024.

Rugulies R, Aust B, Greiner BA, Arensman E, Kawakami N, LaMontagne AD, et al. Work-related causes of mental health conditions and interventions for their improvement in workplaces. Lancet. 2023;402(10410):1368–81.

Mento C, Silvestri MC, Bruno A, Muscatello MRA, Cedro C, Pandolfo G, et al. Workplace violence against healthcare professionals: a systematic review. Aggress Violent Beh. 2020;51:101381.

Google Scholar 

Quigley DD, Qureshi N, Gahlon G, Gidengil C. Worker and employer experiences with COVID-19 and the California workers compensation system: a review of the literature. Am J Ind Med. 2022;65(3):203–13.

CAS  PubMed  PubMed Central  Google Scholar 

American federation of labor and congress of industrial organizations. Death on the Job: The Toll of Neglect, 2022. 2022.

National Safety Council. Work-related fatality trends. 2022.

Bureau of Labor Statistics. National Census of fatal occupational injuries in 2022. 2023.

Tucker S, Keefe A. 2025 report on work fatality and injury rates in Canada. 2025.

Kavlakoglu E. Ai vs. machine learning vs. deep learning vs. neural networks: What’s the difference?: IBM Cloud Education; 2020. Available from: https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks. Accessed 4 Apr 2023.

Brown S. Machine learning, explained. 2021. Available from: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained. Accessed 1 Nov 2023.

Eloundou T, Manning S, Mishkin P, Rock D. Gpts are gpts: an early look at the labor market impact potential of large language models. arXiv preprint arXiv:230310130. 2023.

Wei W, Li L. The impact of artificial intelligence on the mental health of manufacturing workers: the mediating role of overtime work and the work environment. Front Public Health. 2022;10:862407.

Sevilla J, Heim L, Ho A, Besiroglu T, Hobbhahn M, Villalobos P, editors. Compute trends across three eras of machine learning. 2022 International Joint Conference on Neural Networks (IJCNN); 2022 18–23 July 2022.

Li W, Jia F, Hu Q. Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. Journal of Computer and Communications. 2015;3(11):146–51.

Google Scholar 

Yee OS, Sagadevan S, Malim NHAH. Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2018;10(1–4):23–7.

Google Scholar 

Navarro PJ, Fernandez C, Borraz R, Alonso D. A machine learning approach to pedestrian detection for autonomous vehicles using high-definition 3D range data. Sensors. 2016;17(1):18.

PubMed  PubMed Central  Google Scholar 

Podgórski D, Majchrzycka K, Dąbrowska A, Gralewicz G, Okrasa M. Towards a conceptual framework of OSH risk management in smart working environments based on smart PPE, ambient intelligence and the Internet of Things technologies. Int J Occup Saf Ergon. 2017;23(1):1–20.

PubMed  Google Scholar 

Antwi-Afari MF, Li H, Wong JK-W, Oladinrin OT, Ge JX, Seo J, et al. Sensing and warning-based technology applications to improve occupational health and safety in the construction industry. Engineering, Construction and Architectural Management. 2019;26(8):1534–52.

Google Scholar 

Barata J, da Cunha PR, editors. Safety is the new black: the increasing role of wearables in occupational health and safety in construction. Business Information Systems. Cham: Springer International Publishing; 2019.

Bernier T, Shah A, Ross LE, Logie CH, Seto E. The use of information and communication technologies by sex workers to manage occupational health and safety: scoping review. J Med Internet Res. 2021;23(6): e26085.

PubMed  PubMed Central  Google Scholar 

Laroche E, L’Espérance S, Mosconi E. Use of social media platforms for promoting healthy employee lifestyles and occupational health and safety prevention: a systematic review. Saf Sci. 2020;131: 104931.

PubMed  PubMed Central  Google Scholar 

Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA. 2019;322(24):2377–8.

PubMed  Google Scholar 

Institute for Work & Health. Systematic Review Program: How we do systematic reviews. Available from: https://www.iwh.on.ca/systematic-review-program/methods. Accessed 1 Oct 2023.

DistillerSr. DistillerSR Version 2.35 [software]: DistillerSR Inc; [2023]. Available from: https://www.distillersr.com/. Accessed 1 Apr 2024.

Irvin E, Van Eerd D, Amick Iii BC, Brewer S. Introduction to special section: systematic reviews for prevention and management of musculoskeletal disorders. J Occup Rehabil. 2010;20(2):123–6.

PubMed  Google Scholar 

Anan T, Kajiki S, Oka H, Fujii T, Kawamata K, Mori K, et al. Effects of an artificial intelligence–assisted health program on workers with neck/shoulder pain/stiffness and low back pain: randomized controlled trial. JMIR Mhealth Uhealth. 2021;9(9): e27535.

PubMed  PubMed Central  Google Scholar 

Jetha A, Bakhtari H, Rosella LC, Gignac MA, Biswas A, Shahidi FV, et al. Artificial intelligence and the work–health interface: a research agenda for a technologically transforming world of work. Am J Ind Med. 2023;66(10):815–30.

PubMed  Google Scholar 

Rosenfield PL. The potential of transdisciplinary research for sustaining and extending linkages between the health and social sciences. Soc Sci Med. 1992;35(11):1343–57.

CAS  PubMed  Google Scholar 

Smith PM. A transdisciplinary approach to research on work and health: what is it, what could it contribute, and what are the challenges? Crit Public Health. 2007;17(2):159–69.

Google Scholar 

Wei H, Rahman MA, Hu X, Zhang L, Guo L, Tao H, et al. Robotic mounted rail arm system for implementing effective workplace safety for migrant workers. Work. 2021;68:845–52.

PubMed  Google Scholar 

Walters D, Omran J, Patel M, Reeves R, Ang L, Mahmud E. Robotic-assisted percutaneous coronary intervention: concept, data, and clinical application. Interventional Cardiology Clinics. 2019;8(2):149–59.

PubMed  Google Scholar 

Brynjolfsson E, Li D, Raymond LR. Generative AI at work. National Bureau of Economic Research; 2023.

Bengio Y, Hinton G, Yao A, Song D, Abbeel P, Darrell T, et al. Managing extreme AI risks amid rapid progress. Science. 2024:eadn0117.

Howard J, Murashov V, Cauda E, Snawder J. Advanced sensor technologies and the future of work. Am J Ind Med. 2022;65(1):3–11.

PubMed  Google Scholar 

Rahman H. Gig workers are increasingly rated by opaque algorithms. It’s making them paranoid Boston (MA): KelloggInsight: Northwestern University; 2021. Available from: https://insight.kellogg.northwestern.edu/article/gig-workers-algorithm. Accessed 4 Apr 2023.

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–53.

CAS  PubMed  Google Scholar 

Corvite S, Roemmich K, Rosenberg TI, Andalibi N. Data subjects perspectives on emotion artificial intelligence use in the workplace: a relational ethics lens. Proceedings of the ACM on Human-Computer Interaction. 2023;7(CSCW1):1–38.

Google Scholar 

Park YJ, Jones-Jang SM. Surveillance, security, and AI as technological acceptance. AI & Soc. 2023;38(6):2667–78.

Google Scholar 

Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, et al. Emergent abilities of large language models. Transactions on Machine Learning Research. 2022;1(1):1–30.

Google Scholar 

Elliott JH, Synnot A, Turner T, Simmonds M, Akl EA, McDonald S, et al. Living systematic review: 1. Introduction—the why, what, when, and how. Journal of clinical epidemiology. 2017;91:23–30.

PubMed  Google Scholar 

Comments (0)

No login
gif