Clinical Applications of Artificial Intelligence in Occupational Health: A Systematic Literature Review.

Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9504688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-5948 (Electronic) Linking ISSN: 10762752 NLM ISO Abbreviation: J Occup Environ Med Subsets: MEDLINE

Publication: Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: Baltimore, MD : Williams & Wilkins, c1995-

Artificial Intelligence* ; Occupational Health*; Humans ; Risk Assessment/methods ; Return to Work ; Algorithms ; Neural Networks, Computer

Objectives: The aims of the study are to identify and to critically analyze studies using artificial intelligence (AI) in occupational health.
Methods: A systematic search of PubMed, IEEE Xplore, and Web of Science was conducted to identify relevant articles published in English between January 2014-January 2024. Quality was assessed with the validated APPRAISE-AI tool.
Results: The 27 included articles were categorized as follows: health risk assessment ( n = 17), return to work and disability duration ( n = 5), injury severity ( n = 3), and injury management ( n = 2). Forty-seven AI algorithms were utilized, with artificial neural networks, support vector machines, and random forest being most common. Model accuracy ranged from 0.60-0.99 and area under the curve (AUC) from 0.7-1.0. Most studies ( n = 15) were of moderate quality.
Conclusions: While AI has potential clinical utility in occupational health, explainable models that are rigorously validated in real-world settings are warranted.
Competing Interests: Choudhury and Chaudhry have no relationships/conditions/circumstances that present potential conflict of interest.
(Copyright © 2024 American College of Occupational and Environmental Medicine.)

Younis HA, Eisa TAE, Nasser M, et al. A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges. Diagnostics 2024;14:109.
Garcia P, Ma SP, Shah S, et al. Artificial intelligence-generated draft replies to patient inbox messages. JAMA Netw Open 2024;7:e243201.
Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform 2020;8:e18599.
Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc 2023;30:2050–2063.
Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med 2023;29:1930–1940.
Yang X, Chen A, PourNejatian N, et al. A large language model for electronic health records. NPJ Digit Med 2022;5:194.
Harrer S. Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. EBioMedicine 2023;90:104512.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. PLoS Med 2021;18:e1003583.
Chaudhry ZC, Choudhury A. Clinical applications of artificial intelligence in occupational health: a systematic literature review. Open Science Framework 2024. Available at: https://doi.org/10.17605/OSF.IO/WMSYP . Accessed April 20, 2024. (PMID: 10.17605/OSF.IO/WMSYP)
Kwong JCC, Khondker A, Lajkosz K, et al. APPRAISE-AI tool for quantitative evaluation of AI studies for clinical decision support. JAMA Netw Open 2023;6:e2335377.
Abad A, Gerassis S, Saavedra Á, Giráldez E, García JF, Taboada J. A Bayesian assessment of occupational health surveillance in workers exposed to silica in the energy and construction industry. Environ Sci Pollut Res 2019;26:29560–29569.
Aliabadi M, Farhadian M, Darvishi E. Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach. Int Arch Occup Environ Health 2015;88:779–787.
Anan T, Kajiki S, Oka H, 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:e27535.
Armstrong DP, Ross GB, Graham RB, Fischer SL. Considering movement competency within physical employment standards. Work 2019;63:603–613.
Bai Z, Zhang J, Tang C, et al. Return-to-work predictions for Chinese patients with occupational upper extremity injury: a prospective cohort study. Front Med 2022;9:805230.
Cui S, Li C, Chen Z, Wang J, Yuan J. Research on risk prediction of dyslipidemia in steel workers based on recurrent neural network and LSTM neural network. IEEE Access 2020;8:34153–34161.
Darvishi E, Khotanlou H, Khoubi J, Giahi O, Mahdavi N. Prediction effects of personal, psychosocial, and occupational risk factors on low back pain severity using artificial neural networks approach in industrial workers. J Manipulative Physiol Ther 2017;40:486–493.
Eyvazlou M, Hosseinpouri M, Mokarami H, et al. Prediction of metabolic syndrome based on sleep and work-related risk factors using an artificial neural network. BMC Endocr Disord 2020;20:169.
Gerassis S, Boente C, Albuquerque MTD, Ribeiro MM, Abad A, Taboada J. Mapping occupational health risk factors in the primary sector—a novel supervised machine learning and area-to-point Poisson kriging approach. Spatial Statistics 2021;42:100434.
Gross DP, Steenstra IA, Shaw W, Yousefi P, Bellinger C, Zaïane O. Validity of the Work Assessment Triage Tool for selecting rehabilitation interventions for workers’ compensation claimants With musculoskeletal conditions. J Occup Rehabil 2020;30:318–330.
Kakhki DF, Freeman SA, Mosher GA. Evaluating machine learning performance in predicting injury severity in agribusiness industries. Safety Science 2019;117:257–262.
Kakhki FD, Freeman SA, Mosher GA. Applied machine learning in agro-manufacturing occupational incidents. Procedia Manufacturing 2020;48:24–30.
Khairuddin MZF, Hasikin K, Razak NAA, Mohshim SA, Ibrahim SS. Harnessing the multimodal data integration and deep learning for occupational injury severity prediction. IEEE Access 2023;11:85284–85302.
Lee J, Kim HR. Prediction of return-to-original-work after an industrial accident using machine learning and comparison of techniques. J Korean Med Sci 2018;33:e144.
Mollaei N, Fujao C, Silva L, Rodrigues J, Cepeda C, Gamboa H. Human-centered explainable artificial intelligence: automotive occupational health protection profiles in prevention musculoskeletal symptoms. IJERPH 2022;19:9552.
Na KS, Kim E. A machine learning-based predictive model of return to work after sick leave. J Occup Environ Med 2019;61:e191–e199.
Ni L, Chen F, Ran R, et al. A deep learning-based model for predicting abnormal liver function in workers in the automotive manufacturing industry: a cross-sectional survey in Chongqing, China. IJERPH 2022;19:14300.
Ötleş E, Seymour J, Wang H, Denton BT. Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations. J Am Med Inform Assoc 2022;29:1931–1940.
Uronen L, Salanterä S, Hakala K, Hartiala J, Moen H. Combining supervised and unsupervised named entity recognition to detect psychosocial risk factors in occupational health checks. Int J Med Inform 2022;160:104695.
Wang J, Li C, Li J, et al. Development and internal validation of risk prediction model of metabolic syndrome in oil workers. BMC Public Health 2020;20:1828.
Wu J, Qin S, Wang J, et al. Develop and evaluate a new and effective approach for predicting dyslipidemia in steel workers. Front Bioeng Biotechnol 2020;8:839.
Xin Y, Wang B, Zhang H, et al. Machine learning assessment of white blood cell counts in workers exposed to benzene: a historical cohort study. Environ Sci Pollut Res 2022;30:38202–38211.
Yang HY. Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals. PLoS One 2019;14:e0214808.
Yedla A, Kakhki FD, Jannesari A. Predictive modeling for occupational safety outcomes and days away from work analysis in mining operations. IJERPH 2020;17:7054.
Zhang Y, Zhang Y, Liu B, Meng X. Prediction of the length of service at the onset of coal workers’ pneumoconiosis based on neural network. Arch Environ Occup Health 2020;75:242–250.
Zhao Y, Li J, Zhang M, et al. Machine learning models for the hearing impairment prediction in workers exposed to complex industrial noise: a pilot study. Ear Hear 2019;40:690–699.
Zhou M, Bian K, Hu F, Lai W. A new strategy for identification of coal miners with abnormal physical signs based on EN-mRMR. Front Bioeng Biotechnol 2022;10:935481.
Occupational Safety and Health Administration. Medical screening and surveillance requirements in OSHA standards: a guide. U.S. Department of Labor; 2014. OSHA 316201R. Available at: https://www.osha.gov/sites/default/files/publications/osha3162.pdf . Accessed July 31, 2024.
Amann J, Blasimme A, Vayena E, Frey D, Madai VI; Precise4Q Consortium. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 2020;20:310.

Date Created: 20240827 Date Completed: 20241202 Latest Revision: 20241205

20241209

10.1097/JOM.0000000000003212

39190393