Journal of Health Sciences & Surveillance System

A Systematic Review of the Applications of Artificial Intelligence in the Study of Occupational Cancers

Document Type : Review Articles

Authors

Zohreh Fazli 1, 2
Reza Pourbabaki 3
Nahid Fazli 4
Esmaeel Soleimani 5

1 Department of Occupational Health and Safety Engineering, School of Public Health, Bam University of Medical Sciences, Bam, Iran

2 Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

3 Department of Occupational Health Engineering, Sirjan School of Medical Sciences, Sirjan, Iran

4 Ilam Central Laboratory, Ilam University of Medical Sciences, Ilam, Iran

5 Department of Occupational Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

https://doi.org/10.30476/jhsss.2025.105913.2032
Abstract
Background: Occupational exposures contribute to approximately 8% of all cancer cases. Artificial intelligence (AI), encompassing machine learning and deep learning, presents a promising avenue for the prevention and management of these cancers. This systematic review explores the current applications of AI in the detection, assessment, diagnosis, and control of occupational cancers.
Methods: This systematic review adhered to PRISMA guidelines, conducting a thorough search of the Web of Science, Scopus, and PubMed databases between October and November 2023. The search utilized keywords related to occupation, cancer, and AI. Articles were included if they examined occupational cancers, occupational exposure to carcinogens, and the application of 
AI tools. A screening process was followed by data extraction, focusing on study details, populations, and AI applications.
Results: The initial search yielded 2426 articles, from which 34 were selected for full-text review. Ultimately, 23 studies were included in the analysis. These studies covered a range of occupational cancers and AI applications, broadly categorized into: 1) AI for risk assessment of occupational exposure to carcinogens, 2) AI for cancer prediction and detection, and 3) AI for cancer diagnosis and clinical evaluation. Lung cancer was the most frequently studied type of occupational cancer.
Conclusion: The application of AI in the study of occupational cancers is relatively nascent, with a limited number of studies identified. However, given AI’s significant capabilities in disease assessment and management, future research is strongly recommended to leverage these tools for investigating the most prevalent occupational cancers, including lung, bladder, laryngeal, leukemia, and liver cancers.

Keywords

Artificial Intelligence
Cancer
Deep Learning
Machine Learning
Occupation
  1. Rezaei A, Ghafari ME, Sohrabi Y, Aliasghari F, Yousefinejad S, Soleimani E, et al. Systemic inflammation indices as hematological biomarkers of inflammatory response in non-silicotic workers exposed to respirable silica dust. Toxicol Lett. 2024;395:26-39. doi: 10.1016/j.toxlet.2024.03.005.
  2. Sohrabi Y, Rahimian F, Soleimani E, Hassanipour S. Low-level occupational exposure to BTEX and dyschromatopsia: a systematic review and meta-analysis. Int J Occup Saf Ergon. 2024;30(1):9-19. doi: 10.1080/10803548.2022.2157543.
  3. Kooshki F, Neghab M, Rahimian F, Aliasghari F, Soleimani E. Occupational Exposure to Low Concentrations of Lead Dust and Oxidative Stress in Mine Workers. J Health Sci Surveill Syst. 2025;13(1):83-9. doi: 10.30476/jhsss.2024.100426.1836.
  4. Mohammadian F, Sadeghi M, Hanifi SM, Noorizadeh N, Abedi K, Fazli Z. Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm. Work. 2022;73(1):189-202. doi: 10.3233/WOR-205271. 
  5. Sohrabi Y, Sabet S, Yousefinejad S, Rahimian F, Aryaie M, Soleimani E, et al. Pulmonary function and respiratory symptoms in workers exposed to respirable silica dust: A historical cohort study. Heliyon. 2022;8(11). doi: 10.1016/j.heliyon.2022.e11642. 
  6. Kooshki F, Neghab M, Soleimani E, Hasanzadeh J. Low-level exposure to lead dust in unusual work schedules and hematologic, renal, and hepatic parameters. Toxicol Appl Pharmacol. 2021;415:115448. doi: 10.1016/j.taap.2021.115448.
  7. Girigoswami K, Saini D, Girigoswami A. Extracellular matrix remodeling and development of cancer. Stem Cell Rev Rep. 2021;17:739-47. doi: 10.1007/s12015-020-10070-1.
  8. Crimini E, Repetto M, Tarantino P, Ascione L, Antonarelli G, Rocco EG, et al. Challenges and obstacles in applying therapeutical indications formulated in molecular tumor boards. Cancers. 2022;14(13):3193. doi: 10.3390/cancers14133193.
  9. Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Piñeros M, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144(8):1941-53. doi: 10.1002/ijc.31937.
  10. Micallef CM, Bonaldi L, Dubois S, Ronga-Pezeriet P, Lemarchand C, Gislard A, et al. Cancers in France in 2015 attributable to occupational exposures. Int J Hyg Environ Health. 2019;222(1):22-9. doi: 10.1016/j.ijheh.2018.07.015.
  11. Huang J, Chan SC, Tin MS, Liu X, Lok V, Ngai CH, et al. Worldwide distribution, risk factors, and temporal trends of testicular cancer incidence and mortality: A global analysis. Eur Urol Oncol. 2022;5(5):566-76. doi: 10.1016/j.euo.2022.06.009.
  12. Wong S, Slavcev R. Treating cancer with infection: a review on bacterial cancer therapy. Lett Appl Microbiol. 2015;61(2):107-12. doi: 10.1111/lam.12436. 
  13. Roos E, Seppä K, Ryynänen H, Heikkinen S, Männistö S, Rissanen H, et al. Pairwise association of key lifestyle factors and risk of solid cancers A prospective pooled multi-cohort register study. Prev Med Rep. 2024;38:102607. doi: 10.1016/j.pmedr.2024.102607.
  14. Seo S, Lee D, Seong KM, Park S, Kim SG, Won JU, et al. Radiation-related occupational cancer and its recognition criteria in South Korea. Ann Occup Environ Med. 2018;30(1):1-11. doi:10.1186/s40557-018-0219-y. 
  15. Paris L, Scarselli A, Iavicoli S, Massari S, Frigeri A, Rondinone BM, et al. Assessment of Occupational Carcinogenic Risk by Comparing Data from the Italian Register of Occupational Exposures to Carcinogens (SIREP) with the International Agency for Research on Cancer (IARC) Evidence. Int J Environ Res Public Health. 2023;20(10):5850. doi: 10.3390/ijerph20105850. 
  16. Pourbabaki R, Siadat SA, Yousefinejad S, Soleimani E. Genetic polymorphisms of GSTM1 and GSTT1 genes: effects on susceptibility to formaldehyde-induced hematotoxicity. Mol Biol Res Commun. 2026;15(1):71-91. doi: 10.22099/mbrc.2025.54313.2213.
  17. Loomis D, Guha N, Hall AL, Straif K. Identifying occupational carcinogens: an update from the IARC Monographs. Occup Environ Med. 2018;75(8):593-603. doi.org/10.1136/oemed-2017-104944. 
  18. Labrèche F, Kim J, Song C, Pahwa M, Ge CB, Arrandale VH, et al. The current burden of cancer attributable to occupational exposures in Canada. Prev Med. 2019;122:128-39. doi: 10.1016/j.ypmed.2019.03.016. 
  19. Froń A, Semianiuk U, Lazuk U, Małkiewicz T, Adamczyk P, Służała A, et al. Artificial Intelligence in Urooncology: What We Have and What We Expect. Cancers. 2023;15(17):4282. doi: 10.3390/cancers15174282. 
  20. Kantarjian H, Yu PP. Artificial intelligence, big data, and cancer. JAMA Oncol. 2015;1(5):573-4. doi: 10.1001/jamaoncol.2015.1203.  
  21. Elemento O, Leslie C, Lundin J, Tourassi G. Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer. 2021;21(12):747-52. doi: 10.1038/s41568-021-00399-1. 
  22. Hunter B, Hindocha S, Lee RW. The role of artificial intelligence in early cancer diagnosis. Cancers. 2022;14(6):1524. doi: 10.3390/cancers14061524.
  23. Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med. 2023;165:107356. doi: 10.1016/j.compbiomed.2023.107356. 
  24. Silva HECd, Santos GNMd, Leite AF, Mesquita CR, Figueiredo PTd, Melo NSd, et al. The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews. PLoS One. 2023;18(10):e0292063. doi: 10.1371/journal.pone.0292063. 
  25. Parekh ADE, Shaikh OA, Simran, Manan S, Hasibuzzaman MA. Artificial intelligence (AI) in personalized medicine: AI-generated personalized therapy regimens based on genetic and medical history: short communication. Ann Med Surg (Lond). 2023;86(2):1245-7. doi: 10.1097/MS9.0000000000001320. 
  26. Wojtara M, Rana E, Rahman T, Khanna P, Singh H. Artificial intelligence in rare disease diagnosis and treatment. Clin Transl Sci. 2023;16(11):2106-11. doi: 10.1111/cts.13619. 
  27. Alshuhri MS, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, et al. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract. 2023;253:154996. doi: 10.1016/j.prp.2023.154996. 
  28. Chaddad A, Katib Y, Hassan L. Future artificial intelligence tools and perspectives in medicine. Curr Opin Urol. 2021;31(4):371-7. doi: 10.1097/MOU.0000000000000884. 
  29. Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, et al. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat Med. 2022;28(6):1232-9. doi: 10.1038/s41591-022-01768-5. 
  30. Wong JYY, Imani P, Grigoryan H, Bassig BA, Dai Y, et al. Exposure to diesel engine exhaust and alterations to the Cys34/Lys525 adductome of human serum albumin. Environ Toxicol Pharmacol. 2022;95:103966. doi: 10.1016/j.etap.2022.103966. 
  31. Aquilina NJ, Delgado-Saborit JM, Gauci AP, Baker S, Meddings C, Harrison RM. Comparative Modeling Approaches for Personal Exposure to Particle-Associated PAH. Environ Sci Technol. 2010;44(24):9370-6. doi: 10.1021/es102529k. 
  32. Tomiazzi JS, Judai MA, Nai GA, Pereira DR, Antunes PA, Favareto APA. Evaluation of genotoxic effects in Brazilian agricultural workers exposed to pesticides and cigarette smoke using machine-learning algorithms. Environ Sci Pollut Res. 2018;25(2):1259-69. doi: 10.1007/s11356-017-0496-y. 
  33. Ladeira C, Araújo R, Ramalhete L, Teixeira H, Calado CRC. Blood molecular profile to predict genotoxicity from exposure to antineoplastic drugs. Mutat Res Genet Toxicol Environ Mutagen. 2023;891:503681. doi: 10.1016/j.mrgentox.2023.503681.  
  34. Borroni E, Frigerio G, Polledri E, Mercadante R, Maggioni C, Fedrizzi L, et al. Metabolomic profiles in night shift workers: A cross-sectional study on hospital female nurses. Front Public Health. 2023;11. doi: 10.3389/fpubh.2023.1082074. 
  35. Yang HY. Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals. PLoS One. 2019;14(4):e0214808. doi: 10.1371/journal.pone.0214808.
  36. Shakhssalim N, Talebi A, Pahlevan-Fallahy MT, Sotoodeh K, Alavimajd H, Borumandnia N, et al. Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models. Cancer Rep (Hoboken). 2023;6(9):e1860. doi: 10.1002/cnr2.1860. 
  37. Rana HK, Akhtar MR, Islam MB, Ahmed MB, Lió P, Huq F, et al. Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression. Sci Rep. 2020;10(1):2795. doi: 10.1038/s41598-020-57916-9. 
  38. Anderson O, Kidd AC, Goatman KA, Weir AJ, Voisey J, Dilys V, et al. Fully Automated Volumetric Measurement of Malignant Pleural Mesothelioma from Computed Tomography Images by Deep Learning: Preliminary Results of an Internal Validation. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOIMAGING). 2020. p. 64-73. doi: 10.5220/0008976100640073.
  39. Ranjan A, Zhao Y, Sahu HB, Misra P. Opportunities and challenges in health sensing for extreme industrial environment: Perspectives from underground mines. IEEE Access. 2019;7:139181-95. doi: 10.1109/ACCESS.2019.2943289. 
  40. Rugard M, Coumoul X, Carvaillo JC, Barouki R. Deciphering Adverse Outcome Pathway Network Linked to Bisphenol F Using Text Mining and Systems Toxicology Approaches. Toxicol Sci. 2020;173(1):32-40. doi: 10.1093/toxsci/kfz214. 
  41. Jovanovic G, Perisic M, Bacanin N, Zivkovic M, Stanisic S, Strumberger I, et al. Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate. Toxics. 2023;11(4):394. doi: 10.3390/toxics11040394.
  42. Fletcher CME, Trenerry C, Wilson C, Gunn KM. 'Being a farmer, I mostly always think there is something more important to do': A mixed methods analysis of the skin cancer detection practices of Australian farmers. Health Promot J Austr. 2024;35(1):147-56. doi: 10.1002/hpja.796. 
  43. Hakkarainen AJ, Randen-Brady R, Wolff H, Mäyränpää MI, Sajantila A. Deep learning neural network -guided detection of asbestos bodies in bronchoalveolar lavage samples. Acta Cytol. 2023;67(6):624-31. doi: 10.1159/000534149. 
  44. Rana HK, Akhtar MR, Islam MB, Ahmed MB, Lió P, Huq F, et al. Machine learning and bioinformatics models to identify pathways that mediate influences of welding fumes on cancer progression. Sci Rep. 2020;10(1):2795. doi: 10.1038/s41598-020-57916-9. 
  45. Rezaali M, Fouladi-Fard R, Mojarad H, Sorooshian A, Mahdinia M, Mirzaei N. A wavelet-based random forest approach for indoor BTEX spatiotemporal modeling and health risk assessment. Environ Sci Pollut Res. 2021;28:22522-35. doi: 10.1007/s11356-020-12298-3. 
  46. Shahid S, Masood K, Khan AW. Prediction of impacts on liver enzymes from the exposure of low-dose medical radiations through artificial intelligence algorithms. Rev Assoc Med Bras. 2021;67(2):248-59. doi: 10.1590/1806-9282.67.02.20200653. 
  47. Limbu S, Dakshanamurthy S. Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method. Sensors. 2022;22(21):8185. doi: 10.3390/s22218185. 
  48. Zhao F, Zhang H, Cheng D, Wang W, Li Y, Wang Y, et al. Predicting the risk of nodular thyroid disease in coal miners based on different machine learning models. Front Med (Lausanne). 2022;9:1037944. doi: 10.3389/fmed.2022.1037944. 
  49. Badreau M, Rapicault C, Porro B, Descatha A, Fadel M. Comparison of Machine Learning Methods in the Study of Cancer Survivors' Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort. J Occup Rehabil. 2023;33:574-86. doi: 10.1007/s10926-023-10112-8. 
  50. Kidd AC, Anderson O, Cowell GW, Weir AJ, Voisey JP, Evison M, et al. Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria. Thorax. 2022;77(12):1251-9. doi: 10.1136/thoraxjnl-2021-217808. 
  51. Benlala I, de Senneville BD, Dournes G, Menant M, Gramond C, Thaon I, et al. Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects. Int J Environ Res Public Health. 2022;19(3):1417. doi: 10.3390/ijerph19031417. 
  52. Gupta S, Gupta MK. Computational Model for Prediction of Malignant Mesothelioma Diagnosis. Comput J. 2023;66(1):86-100. doi: 10.1093/comjnl/bxab146. 
  53. Devnath L, Luo S, Summons P, Wang D, Shaukat K, Hameed IA, et al. Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography. J Clin Med. 2022;11(18):5342. doi: 10.3390/jcm11185342.
  54. Zhang J, Cui S, Shen L, Wang Q, Rong R, Zhang T, et al. Promotion of Bladder Cancer Cell Metastasis by 2-Mercaptobenzothiazole via Its Activation of Aryl Hydrocarbon Receptor Transcription: Molecular Dynamics Simulations, Cell-Based Assays, and Machine Learning-Driven Prediction. Environ Sci Technol. 2022;56(18):13254-63. doi: 10.1021/acs.est.2c05178.
Journal of Health Sciences & Surveillance System
Volume 14, Issue 2
Spring 2026
Pages 111-120

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