Journal of Occupational
Health and Epidemiology
Rafsanjan university Of medical sciences
Volume 14, Issue 3 (Summer 2025)
J Occup Health Epidemiol 2025, 14(3): 214-225 |
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Shakerian M, Barakat S, Saber E. Risk Management of Work-Related Musculoskeletal Disorders Using an Artificial Intelligence Approach (Narrative Review). J Occup Health Epidemiol 2025; 14 (3) :214-225
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1- Assistant Prof, Dept. of Occupational Health and safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
2- Student Research Committee, Dept. of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran. ,samirabarakat.ohs@gmail.com
3- Student Research Committee, Dept. of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
2- Student Research Committee, Dept. of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran. ,
3- Student Research Committee, Dept. of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Article history
Received: 2025/01/17
Accepted: 2025/05/2
ePublished: 2025/09/28
Accepted: 2025/05/2
ePublished: 2025/09/28
Subject:
Occupational Health
Abstract: (154 Views)
Background: In today's world, it is crucial to apply methods that help predict, assess, and prevent work-related musculoskeletal disorders (WMSDs) within the framework of risk management. The advent of artificial intelligence (AI) has introduced a new perspective on employee occupational health and safety. This study aimed to manage the risk of musculoskeletal disorders based on an AI-driven approach.
Materials and Methods: In the present study, the databases ISI Web of Science, Scopus, Medline (via PubMed), Science Direct, and Google Scholar were used. The keywords for the article searches included 'work-related musculoskeletal disorders (WMSDs)', 'artificial intelligence (AI)', 'WMSDs risk management', 'WMSDs prediction', 'WMSDs risk assessment', and 'WMSDs prevention'.
Results: Studies have indicated that the application of AI for risk management—specifically in the prediction, identification, evaluation, and prevention of musculoskeletal disorders—has proven to be both effective and beneficial. Further, AI helps managers monitor the health status of their workforce and avoid placing excessive pressure on employees. Nevertheless, as with any new tool, AI has its drawbacks in risk management for disruptions.
Conclusions: The application of AI in WMSDs risk management warrants further investigation and testing to ensure that AI algorithms can be developed into a single, standardized solution with high reliability and accuracy for managing WMSDs risks.
Materials and Methods: In the present study, the databases ISI Web of Science, Scopus, Medline (via PubMed), Science Direct, and Google Scholar were used. The keywords for the article searches included 'work-related musculoskeletal disorders (WMSDs)', 'artificial intelligence (AI)', 'WMSDs risk management', 'WMSDs prediction', 'WMSDs risk assessment', and 'WMSDs prevention'.
Results: Studies have indicated that the application of AI for risk management—specifically in the prediction, identification, evaluation, and prevention of musculoskeletal disorders—has proven to be both effective and beneficial. Further, AI helps managers monitor the health status of their workforce and avoid placing excessive pressure on employees. Nevertheless, as with any new tool, AI has its drawbacks in risk management for disruptions.
Conclusions: The application of AI in WMSDs risk management warrants further investigation and testing to ensure that AI algorithms can be developed into a single, standardized solution with high reliability and accuracy for managing WMSDs risks.
Keywords: Musculoskeletal Disorders [MeSH], Risk Management [MeSH], Artificial Intelligence [MeSH], Prediction , Risk Assessment
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