Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks
Main Article Content
Keywords
Artificial Intelligence, Occupational Health, Safety, Mental Health, Hazard, Wearable Devices
Abstract
This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although the integration of AI into the field of occupational health and safety is still in its early stages, it has numerous applications in the workplace. Some of these applications offer numerous benefits for the health and safety of workers, such as continuous monitoring of workers' health and safety and the workplace environment through wearable devices and sensors. However, AI might have negative impacts in the workplace, such as ethical worries and data privacy concerns. To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.
References
1. Howard J. Algorithms and the future of work. Am J Ind Med. 2022;65(12):943-952. Doi: 10.1002/ajim.23429
2. Georgieff A, Hyee R. Artificial Intelligence and Employment: New Cross-Country Evidence. Front Artif Intell. 2022;5:832736. Doi: 10.3389/frai.2022.832736
3. Howard J. Artificial intelligence: Implications for the future of work. Am J Ind Med. Nov 2019;62(11):917-926. Doi: 10.1002/ajim.23037
4. Niehaus S, Hartwig M, Rosen PH, Wischniewski S. An Occupational Safety and Health Perspective on Human in Control and AI. Front Artif Intell. 2022;5:868382. Doi: 10.3389/frai.2022.868382
5. Rieth LK. The occupational health service. Staffing, facilities, and equipment. AAOHN J. Aug 2000;48(8):395-403; quiz 404-5.
6. Castillo C, Shahriari M, Casarejos F, Arezes P. Prioritization of leading operational indicators in occupational safety and health. Int J Occup Saf Ergon. 2023;29(2):806-814. Doi:10.1080/10803548.2022.2082693
7. Pishgar M, Issa SF, Sietsema M, Pratap P, Darabi H. REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health. Int J Environ Res Public Health. 2021;18(13). Doi: 10.3390/ijerph18136705
8. Carbonero F, Davies J, Ernst E, Fossen FM, Samaan D, Sorgner A. The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam. J Evol Econ. 2023:1-30. Doi: 10.1007/s00191-023-00809-7
9. Djebrouni M, Wolbring G. Impact of robotics and human enhancement on occupation: what does it mean for rehabilitation? Disabil Rehabil. 2020;42(11):1518-1528. Doi: 10.1080/09638288.2018.1527401
10. Hazarika I. Artificial intelligence: opportunities and implications for the health workforce. Int Health. Jul 1 2020;12(4):241-245. Doi: 10.1093/inthealth/ihaa007
11. Warning A, Weber E, Puffel A. On the Impact of Digitalization and Artificial Intelligence on Employers' Flexibility Requirements in Occupations-Empirical Evidence for Germany. Front Artif Intell. 2022;5:868789. Doi: 10.3389/frai.2022.868789
12. Steimers A, Schneider M. Sources of Risk of AI Systems. Int J Environ Res Public Health. 2022;19(6). Doi: 10.3390/ijerph19063641
13. Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review. Sensors (Basel). Oct 2 2022;22(19). Doi: 10.3390/s22197472
14. Mondal S, Zehra N, Choudhury A, Iyer PK. Wearable Sensing Devices for Point of Care Diagnostics. ACS Appl Bio Mater. 2021;4(1):47-70. Doi: 10.1021/acsabm.0c00798
15. Kimball JP, Inan OT, Convertino VA, Cardin S, Sawka MN. Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms. Sensors (Basel). 2022;22(2). Doi: 10.3390/s22020442
16. Podgorski D, Majchrzycka K, Dabrowska 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. Doi: 10.1080/10803548.2016.1214431
17. Huang Y, Benford S, Price D, et al. Using Internet of Things to Reduce Office Workers' Sedentary Behavior: Intervention Development Applying the Behavior Change Wheel and Human-Centered Design Approach. JMIR Mhealth Uhealth. 2020;8(7):e17914. Doi: 10.2196/17914
18. Font O, Torrents-Barrena J, Royo D, et al. Validation of an autonomous artificial intelligence-based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context. Graefes Arch Clin Exp Ophthalmol. 2022;260(10):3255-3265. Doi: 10.1007/s00417-022-05653-2
19. Ahmed A, Aziz S, Abd-Alrazaq A, Farooq F, Househ M, Sheikh J. The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review. J Med Internet Res. Mar 14 2023;25:e40259. Doi: 10.2196/40259
20. Ahmed A, Aziz S, Abd-Alrazaq A, Farooq F, Sheikh J. Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review. J Med Internet Res. 2022;24(8):e36010. Doi: 10.2196/36010
21. Beach C, Casson AJ. Impact of shift working on the potential for self-powering via kinetic energy harvesting in wearable devices. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:7003-7006. Doi: 10.1109/EMBC46164.2021.9631066
22. Campero-Jurado I, Marquez-Sanchez S, Quintanar-Gomez J, Rodriguez S, Corchado JM. Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence. Sensors (Basel). Nov 1 2020;20(21). Doi:10.3390/s20216241
23. Greenfield R, Busink E, Wong CP, et al. Truck drivers' perceptions on wearable devices and health promotion: a qualitative study. BMC Public Health. Jul 30 2016;16:677. Doi: 10.1186/s12889-016-3323-3
24. Kim Y, Choi Y. Smart Helmet-Based Proximity Warning System to Improve Occupational Safety on the Road Using Image Sensor and Artificial Intelligence. Int J Environ Res Public Health. Dec 6 2022;19(23). Doi: 10.3390/ijerph192316312
25. Bhat G, Tran N, Shill H, Ogras UY. w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices. Sensors (Basel). 2020;20(18). Doi: 10.3390/s20185356
26. Fukumura YE, Gray JM, Lucas GM, Becerik-Gerber B, Roll SC. Worker Perspectives on Incorporating Artificial Intelligence into Office Workspaces: Implications for the Future of Office Work. Int J Environ Res Public Health. 2021;18(4). Doi: 10.3390/ijerph18041690
27. Devagiri VM, Boeva V, Abghari S, Basiri F, Lavesson N. Multi-View Data Analysis Techniques for Monitoring Smart Building Systems. Sensors (Basel). 2021;21(20). Doi: 10.3390/s21206775
28. Yun J, Won KH. Building environment analysis based on temperature and humidity for smart energy systems. Sensors (Basel). 2012;12(10):13458-70. Doi: 10.3390/s121013458
29. Khairuddin MZF, Lu Hui P, Hasikin K, et al. Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance. Int J Environ Res Public Health. 2022;19(21). Doi: 10.3390/ijerph192113962
30. Kang SY, Min S, Kim WS, Won JH, Kang YJ, Kim S. Types and Characteristics of Fatal Accidents Caused by Multiple Processes in a Workplace: Based on Actual Cases in South Korea. Int J Environ Res Public Health. 2022;19(4). Doi: 10.3390/ijerph19042047
31. Khashaba E, El-Helaly M, El-Gilany AH, Motawei SM, Foda S. Risk factors for non-fatal occupational injuries among construction workers: A case-control study. Toxicol Ind Health. 2018;34(2):83-90. Doi: 10.1177/0748233717733853
32. Johnston KL, Phillips ML, Esmen NA, Hall TA. Evaluation of an artificial intelligence program for estimating occupational exposures. Ann Occup Hyg. 2005;49(2):147-53. Doi: 10.1093/annhyg/meh072
33. Aram SA, Saalidong BM, Appiah A, Utip IB. Occupational health and safety in mining: Predictive probabilities of Personal Protective Equipment (PPE) use among artisanal goldminers in Ghana. PLoS One. 2021;16(9):e0257772. Doi: 10.1371/journal.pone.0257772
34. Lee JY, Park J, Park H, et al. What do firefighters desire from the next generation of personal protective equipment? Outcomes from an international survey. Ind Health. 2015;53(5):434-44. Doi: 10.2486/indhealth.2015-0033
35. Marchal P, Baudoin J. Proposal for a method for analysing smart personal protective systems. Int J Occup Saf Ergon. 2022;28(3):1566-1576. Doi: 10.1080/10803548.2021.1935541
36. Tao H, Rahman MA, Al-Saffar A, et al. Security robot for the prevention of workplace violence using the Non-linear Adaptive Heuristic Mathematical Model. Work. 2021;68(3):853-861. Doi: 10.3233/WOR-203419
37. Byon HD, Harris C, Crandall M, Song J, Topaz M. Identifying Type II workplace violence from clinical notes using natural language processing. Workplace Health Saf. 2023;71(10):484-490. Doi: 10.1177/21650799231176078
38. Swaminathan A, Lopez I, Mar RAG, et al. Natural language processing system for rapid detection and intervention of mental health crisis chat messages. NPJ Digit Med. 2023;6(1):213. Doi: 10.1038/s41746-023-00951-3
39. Banda JM, Seneviratne M, Hernandez-Boussard T, Shah NH. Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models. Annu Rev Biomed Data Sci. 2018;1:53-68. Doi: 10.1146/annurev-biodatasci-080917-013315
40. Kumar PM, Pandey HM, Srivastava G. Special Issue on Workplace Violence Prevention using Security Robots. Work. 2021;68(3):821-823. Doi: 10.3233/WOR-203415
41. Goplerud E, Hodge S, Benham T. A Substance Use Cost Calculator for US Employers With an Emphasis on Prescription Pain Medication Misuse. J Occup Environ Med. 2017;59(11):1063-1071. Doi: 10.1097/JOM.0000000000001157
42. McAndrew KG, McAndrew SJ. Workplace substance abuse impairment: the occupational health care provider's role. AAOHN J. 2000;48(1):32-45; quiz 46-7.
43. Barenholtz E, Fitzgerald ND, Hahn WE. Machine-learning approaches to substance-abuse research: emerging trends and their implications. Curr Opin Psychiatry. 2020;33(4):334-342. Doi: 10.1097/YCO.0000000000000611
44. Islam A, Khan KM, Scarbrough A, et al. An Artificial Intelligence-Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study. JMIR Form Res. 2023;7:e45434. Doi: 10.2196/45434
45. Huang Y, Benford S, Li B, Price D, Blake H. Feasibility and Acceptability of an Internet of Things-Enabled Sedentary Behavior Intervention: Mixed Methods Study. J Med Internet Res. 2023;25:e43502. Doi: 10.2196/43502
46. Andersen LL, Thorsen SV, Flyvholm MA, Holtermann A. Long-term sickness absence from combined factors related to physical work demands: prospective cohort study. Eur J Public Health. 2018;28(5):824-829. Doi: 10.1093/eurpub/cky073
47. El-Helaly M, Balkhy HH, Vallenius L. Carpal tunnel syndrome among laboratory technicians in relation to personal and ergonomic factors at work. J Occup Health. 2017;59(6):513-520. Doi: 10.1539/joh.16-0279-OA
48. Mansoor SN, Al Arabia DH, Rathore FA. Ergonomics and musculoskeletal disorders among health care professionals: Prevention is better than cure. J Pak Med Assoc. 2022;72(6):1243-1245. Doi: 10.47391/JPMA.22-76
49. El-Helaly M, Elsherbeny E, Haji A, Assiri M, Fadlelmula A. Reported musculoskeletal symptoms among laboratory workers, in relation to individual and work-related physical factors. Egy J Occup Med. 2018;42:79-92. Doi: 10.21608/ejom.2018.4940
50. Hulshof CTJ, Pega F, Neupane S, et al. The prevalence of occupational exposure to ergonomic risk factors: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021;146:106157. Doi: 10.1016/j.envint.2020.106157
51. 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. Sep 24 2021;9(9):e27535. Doi: 10.2196/27535
52. Donisi L, Cesarelli G, Pisani N, Ponsiglione AM, Ricciardi C, Capodaglio E. Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature. Diagnostics (Basel). 2022;12(12). Doi: 10.3390/diagnostics12123048
53. Homayounfar SZ, Andrew TL. Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges. SLAS Technol. 2020;25(1):9-24. Doi: 10.1177/2472630319891128
54. Tsai YL, Wadgaonkar C, Chun B, Knight H. How Service Robots Can Improve Workplace Experience: Camaraderie, Customization, and Humans-in-the-Loop. Int J Soc Robot. 2022;14(7):1605-1624. Doi: 10.1007/s12369-022-00898-7
55. Lopes SL, Ferreira AI, Prada R. The Use of Robots in the Workplace: Conclusions from a Health Promoting Intervention Using Social Robots. Int J Soc Robot. 2023:1-13. Doi: 10.1007/s12369-023-01000-5
56. Coghlan S. Robots and the Possibility of Humanistic Care. Int J Soc Robot. 2022;14(10):2095-2108. Doi: 10.1007/s12369-021-00804-7
57. Payne TH. Computer decision support systems. Chest. Aug 2000;118(2 Suppl):47S-52S. Doi: 10.1378/chest.118.2_suppl.47s
58. Nimri R, Piper M, Pinsker JE, Dassau E. Decision Support Systems and Closed Loop. Diabetes Technol Ther. Feb 2020;22(S1):S47-S62. Doi: 10.1089/dia.2020.2504
59. Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial Intelligence Applications in Health Care Practice: Scoping Review. J Med Internet Res. 2022;24(10):e40238. Doi: 10.2196/40238
60. Khan B, Fatima H, Qureshi A, et al. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomed Mater Devices. 2023:1-8. Doi: 10.1007/s44174-023-00063-2
61. Saheb T. "Ethically contentious aspects of artificial intelligence surveillance: a social science perspective". AI Ethics. 2023;3(2):369-379. Doi: 10.1007/s43681-022-00196-y
62. Fisher E, Flynn MA, Pratap P, Vietas JA. Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review. Int J Environ Res Public Health. 2023;20(13). Doi: 10.3390/ijerph20136221
63. Holm JR, Lorenz E. The impact of artificial intelligence on skills at work in Denmark. New Technology, Work & Employment. 2022;37(1):79-101.
64. Chen N, Li Z, Tang B. Can digital skill protect against job displacement risk caused by artificial intelligence? Empirical evidence from 701 detailed occupations. PLoS One. 2022;17(11):e0277280. Doi: 10.1371/journal.pone.0277280
65. Doki S, Sasahara S, Hori D, et al. Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan. BMJ Open. Jun 23 2021;11(6):e046265. Doi: 10.1136/bmjopen-2020-046265
66. Ghani B, Memon KR, Han H, Ariza-Montes A, Arjona-Fuentes JM. Work stress, technological changes, and job insecurity in the retail organization context. Front Psychol. 2022;13:918065. Doi: 10.3389/fpsyg.2022.918065
67. Yang S, Liu K, Gai J, He X. Transformation to Industrial Artificial Intelligence and Workers' Mental Health: Evidence From China. Front Public Health. 2022;10:881827. Doi: 10.3389/fpubh.2022.881827
2. Georgieff A, Hyee R. Artificial Intelligence and Employment: New Cross-Country Evidence. Front Artif Intell. 2022;5:832736. Doi: 10.3389/frai.2022.832736
3. Howard J. Artificial intelligence: Implications for the future of work. Am J Ind Med. Nov 2019;62(11):917-926. Doi: 10.1002/ajim.23037
4. Niehaus S, Hartwig M, Rosen PH, Wischniewski S. An Occupational Safety and Health Perspective on Human in Control and AI. Front Artif Intell. 2022;5:868382. Doi: 10.3389/frai.2022.868382
5. Rieth LK. The occupational health service. Staffing, facilities, and equipment. AAOHN J. Aug 2000;48(8):395-403; quiz 404-5.
6. Castillo C, Shahriari M, Casarejos F, Arezes P. Prioritization of leading operational indicators in occupational safety and health. Int J Occup Saf Ergon. 2023;29(2):806-814. Doi:10.1080/10803548.2022.2082693
7. Pishgar M, Issa SF, Sietsema M, Pratap P, Darabi H. REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health. Int J Environ Res Public Health. 2021;18(13). Doi: 10.3390/ijerph18136705
8. Carbonero F, Davies J, Ernst E, Fossen FM, Samaan D, Sorgner A. The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam. J Evol Econ. 2023:1-30. Doi: 10.1007/s00191-023-00809-7
9. Djebrouni M, Wolbring G. Impact of robotics and human enhancement on occupation: what does it mean for rehabilitation? Disabil Rehabil. 2020;42(11):1518-1528. Doi: 10.1080/09638288.2018.1527401
10. Hazarika I. Artificial intelligence: opportunities and implications for the health workforce. Int Health. Jul 1 2020;12(4):241-245. Doi: 10.1093/inthealth/ihaa007
11. Warning A, Weber E, Puffel A. On the Impact of Digitalization and Artificial Intelligence on Employers' Flexibility Requirements in Occupations-Empirical Evidence for Germany. Front Artif Intell. 2022;5:868789. Doi: 10.3389/frai.2022.868789
12. Steimers A, Schneider M. Sources of Risk of AI Systems. Int J Environ Res Public Health. 2022;19(6). Doi: 10.3390/ijerph19063641
13. Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review. Sensors (Basel). Oct 2 2022;22(19). Doi: 10.3390/s22197472
14. Mondal S, Zehra N, Choudhury A, Iyer PK. Wearable Sensing Devices for Point of Care Diagnostics. ACS Appl Bio Mater. 2021;4(1):47-70. Doi: 10.1021/acsabm.0c00798
15. Kimball JP, Inan OT, Convertino VA, Cardin S, Sawka MN. Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms. Sensors (Basel). 2022;22(2). Doi: 10.3390/s22020442
16. Podgorski D, Majchrzycka K, Dabrowska 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. Doi: 10.1080/10803548.2016.1214431
17. Huang Y, Benford S, Price D, et al. Using Internet of Things to Reduce Office Workers' Sedentary Behavior: Intervention Development Applying the Behavior Change Wheel and Human-Centered Design Approach. JMIR Mhealth Uhealth. 2020;8(7):e17914. Doi: 10.2196/17914
18. Font O, Torrents-Barrena J, Royo D, et al. Validation of an autonomous artificial intelligence-based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context. Graefes Arch Clin Exp Ophthalmol. 2022;260(10):3255-3265. Doi: 10.1007/s00417-022-05653-2
19. Ahmed A, Aziz S, Abd-Alrazaq A, Farooq F, Househ M, Sheikh J. The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review. J Med Internet Res. Mar 14 2023;25:e40259. Doi: 10.2196/40259
20. Ahmed A, Aziz S, Abd-Alrazaq A, Farooq F, Sheikh J. Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review. J Med Internet Res. 2022;24(8):e36010. Doi: 10.2196/36010
21. Beach C, Casson AJ. Impact of shift working on the potential for self-powering via kinetic energy harvesting in wearable devices. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:7003-7006. Doi: 10.1109/EMBC46164.2021.9631066
22. Campero-Jurado I, Marquez-Sanchez S, Quintanar-Gomez J, Rodriguez S, Corchado JM. Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence. Sensors (Basel). Nov 1 2020;20(21). Doi:10.3390/s20216241
23. Greenfield R, Busink E, Wong CP, et al. Truck drivers' perceptions on wearable devices and health promotion: a qualitative study. BMC Public Health. Jul 30 2016;16:677. Doi: 10.1186/s12889-016-3323-3
24. Kim Y, Choi Y. Smart Helmet-Based Proximity Warning System to Improve Occupational Safety on the Road Using Image Sensor and Artificial Intelligence. Int J Environ Res Public Health. Dec 6 2022;19(23). Doi: 10.3390/ijerph192316312
25. Bhat G, Tran N, Shill H, Ogras UY. w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices. Sensors (Basel). 2020;20(18). Doi: 10.3390/s20185356
26. Fukumura YE, Gray JM, Lucas GM, Becerik-Gerber B, Roll SC. Worker Perspectives on Incorporating Artificial Intelligence into Office Workspaces: Implications for the Future of Office Work. Int J Environ Res Public Health. 2021;18(4). Doi: 10.3390/ijerph18041690
27. Devagiri VM, Boeva V, Abghari S, Basiri F, Lavesson N. Multi-View Data Analysis Techniques for Monitoring Smart Building Systems. Sensors (Basel). 2021;21(20). Doi: 10.3390/s21206775
28. Yun J, Won KH. Building environment analysis based on temperature and humidity for smart energy systems. Sensors (Basel). 2012;12(10):13458-70. Doi: 10.3390/s121013458
29. Khairuddin MZF, Lu Hui P, Hasikin K, et al. Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance. Int J Environ Res Public Health. 2022;19(21). Doi: 10.3390/ijerph192113962
30. Kang SY, Min S, Kim WS, Won JH, Kang YJ, Kim S. Types and Characteristics of Fatal Accidents Caused by Multiple Processes in a Workplace: Based on Actual Cases in South Korea. Int J Environ Res Public Health. 2022;19(4). Doi: 10.3390/ijerph19042047
31. Khashaba E, El-Helaly M, El-Gilany AH, Motawei SM, Foda S. Risk factors for non-fatal occupational injuries among construction workers: A case-control study. Toxicol Ind Health. 2018;34(2):83-90. Doi: 10.1177/0748233717733853
32. Johnston KL, Phillips ML, Esmen NA, Hall TA. Evaluation of an artificial intelligence program for estimating occupational exposures. Ann Occup Hyg. 2005;49(2):147-53. Doi: 10.1093/annhyg/meh072
33. Aram SA, Saalidong BM, Appiah A, Utip IB. Occupational health and safety in mining: Predictive probabilities of Personal Protective Equipment (PPE) use among artisanal goldminers in Ghana. PLoS One. 2021;16(9):e0257772. Doi: 10.1371/journal.pone.0257772
34. Lee JY, Park J, Park H, et al. What do firefighters desire from the next generation of personal protective equipment? Outcomes from an international survey. Ind Health. 2015;53(5):434-44. Doi: 10.2486/indhealth.2015-0033
35. Marchal P, Baudoin J. Proposal for a method for analysing smart personal protective systems. Int J Occup Saf Ergon. 2022;28(3):1566-1576. Doi: 10.1080/10803548.2021.1935541
36. Tao H, Rahman MA, Al-Saffar A, et al. Security robot for the prevention of workplace violence using the Non-linear Adaptive Heuristic Mathematical Model. Work. 2021;68(3):853-861. Doi: 10.3233/WOR-203419
37. Byon HD, Harris C, Crandall M, Song J, Topaz M. Identifying Type II workplace violence from clinical notes using natural language processing. Workplace Health Saf. 2023;71(10):484-490. Doi: 10.1177/21650799231176078
38. Swaminathan A, Lopez I, Mar RAG, et al. Natural language processing system for rapid detection and intervention of mental health crisis chat messages. NPJ Digit Med. 2023;6(1):213. Doi: 10.1038/s41746-023-00951-3
39. Banda JM, Seneviratne M, Hernandez-Boussard T, Shah NH. Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models. Annu Rev Biomed Data Sci. 2018;1:53-68. Doi: 10.1146/annurev-biodatasci-080917-013315
40. Kumar PM, Pandey HM, Srivastava G. Special Issue on Workplace Violence Prevention using Security Robots. Work. 2021;68(3):821-823. Doi: 10.3233/WOR-203415
41. Goplerud E, Hodge S, Benham T. A Substance Use Cost Calculator for US Employers With an Emphasis on Prescription Pain Medication Misuse. J Occup Environ Med. 2017;59(11):1063-1071. Doi: 10.1097/JOM.0000000000001157
42. McAndrew KG, McAndrew SJ. Workplace substance abuse impairment: the occupational health care provider's role. AAOHN J. 2000;48(1):32-45; quiz 46-7.
43. Barenholtz E, Fitzgerald ND, Hahn WE. Machine-learning approaches to substance-abuse research: emerging trends and their implications. Curr Opin Psychiatry. 2020;33(4):334-342. Doi: 10.1097/YCO.0000000000000611
44. Islam A, Khan KM, Scarbrough A, et al. An Artificial Intelligence-Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study. JMIR Form Res. 2023;7:e45434. Doi: 10.2196/45434
45. Huang Y, Benford S, Li B, Price D, Blake H. Feasibility and Acceptability of an Internet of Things-Enabled Sedentary Behavior Intervention: Mixed Methods Study. J Med Internet Res. 2023;25:e43502. Doi: 10.2196/43502
46. Andersen LL, Thorsen SV, Flyvholm MA, Holtermann A. Long-term sickness absence from combined factors related to physical work demands: prospective cohort study. Eur J Public Health. 2018;28(5):824-829. Doi: 10.1093/eurpub/cky073
47. El-Helaly M, Balkhy HH, Vallenius L. Carpal tunnel syndrome among laboratory technicians in relation to personal and ergonomic factors at work. J Occup Health. 2017;59(6):513-520. Doi: 10.1539/joh.16-0279-OA
48. Mansoor SN, Al Arabia DH, Rathore FA. Ergonomics and musculoskeletal disorders among health care professionals: Prevention is better than cure. J Pak Med Assoc. 2022;72(6):1243-1245. Doi: 10.47391/JPMA.22-76
49. El-Helaly M, Elsherbeny E, Haji A, Assiri M, Fadlelmula A. Reported musculoskeletal symptoms among laboratory workers, in relation to individual and work-related physical factors. Egy J Occup Med. 2018;42:79-92. Doi: 10.21608/ejom.2018.4940
50. Hulshof CTJ, Pega F, Neupane S, et al. The prevalence of occupational exposure to ergonomic risk factors: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021;146:106157. Doi: 10.1016/j.envint.2020.106157
51. 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. Sep 24 2021;9(9):e27535. Doi: 10.2196/27535
52. Donisi L, Cesarelli G, Pisani N, Ponsiglione AM, Ricciardi C, Capodaglio E. Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature. Diagnostics (Basel). 2022;12(12). Doi: 10.3390/diagnostics12123048
53. Homayounfar SZ, Andrew TL. Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges. SLAS Technol. 2020;25(1):9-24. Doi: 10.1177/2472630319891128
54. Tsai YL, Wadgaonkar C, Chun B, Knight H. How Service Robots Can Improve Workplace Experience: Camaraderie, Customization, and Humans-in-the-Loop. Int J Soc Robot. 2022;14(7):1605-1624. Doi: 10.1007/s12369-022-00898-7
55. Lopes SL, Ferreira AI, Prada R. The Use of Robots in the Workplace: Conclusions from a Health Promoting Intervention Using Social Robots. Int J Soc Robot. 2023:1-13. Doi: 10.1007/s12369-023-01000-5
56. Coghlan S. Robots and the Possibility of Humanistic Care. Int J Soc Robot. 2022;14(10):2095-2108. Doi: 10.1007/s12369-021-00804-7
57. Payne TH. Computer decision support systems. Chest. Aug 2000;118(2 Suppl):47S-52S. Doi: 10.1378/chest.118.2_suppl.47s
58. Nimri R, Piper M, Pinsker JE, Dassau E. Decision Support Systems and Closed Loop. Diabetes Technol Ther. Feb 2020;22(S1):S47-S62. Doi: 10.1089/dia.2020.2504
59. Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial Intelligence Applications in Health Care Practice: Scoping Review. J Med Internet Res. 2022;24(10):e40238. Doi: 10.2196/40238
60. Khan B, Fatima H, Qureshi A, et al. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomed Mater Devices. 2023:1-8. Doi: 10.1007/s44174-023-00063-2
61. Saheb T. "Ethically contentious aspects of artificial intelligence surveillance: a social science perspective". AI Ethics. 2023;3(2):369-379. Doi: 10.1007/s43681-022-00196-y
62. Fisher E, Flynn MA, Pratap P, Vietas JA. Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review. Int J Environ Res Public Health. 2023;20(13). Doi: 10.3390/ijerph20136221
63. Holm JR, Lorenz E. The impact of artificial intelligence on skills at work in Denmark. New Technology, Work & Employment. 2022;37(1):79-101.
64. Chen N, Li Z, Tang B. Can digital skill protect against job displacement risk caused by artificial intelligence? Empirical evidence from 701 detailed occupations. PLoS One. 2022;17(11):e0277280. Doi: 10.1371/journal.pone.0277280
65. Doki S, Sasahara S, Hori D, et al. Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan. BMJ Open. Jun 23 2021;11(6):e046265. Doi: 10.1136/bmjopen-2020-046265
66. Ghani B, Memon KR, Han H, Ariza-Montes A, Arjona-Fuentes JM. Work stress, technological changes, and job insecurity in the retail organization context. Front Psychol. 2022;13:918065. Doi: 10.3389/fpsyg.2022.918065
67. Yang S, Liu K, Gai J, He X. Transformation to Industrial Artificial Intelligence and Workers' Mental Health: Evidence From China. Front Public Health. 2022;10:881827. Doi: 10.3389/fpubh.2022.881827
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Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks. Med Lav [Internet]. 2024 Apr. 24 [cited 2024 Jun. 15];115(2):e2024014. Available from: https://mattioli1885journals.com/index.php/lamedicinadellavoro/article/view/15835
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How to Cite
1.
Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks. Med Lav [Internet]. 2024 Apr. 24 [cited 2024 Jun. 15];115(2):e2024014. Available from: https://mattioli1885journals.com/index.php/lamedicinadellavoro/article/view/15835
