Artificial Intelligence in Workforce Medical Check-Up Programs Across the Employment Lifecycle: A Comprehensive Review
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Abstract
Background: The demographic shift toward aging workforces and the rising prevalence of sedentary occupational disorders demand a paradigm shift in how occupational health is monitored across the employment lifecycle. Artificial intelligence (AI) offers transformative potential for medical check-up (MCU) programs by enabling predictive risk stratification, continuous physiological monitoring, and personalized preventive interventions. Objective: This review synthesizes evidence on AI applications across three workforce stages: pre-employment screening, routine occupational health monitoring, and post-retirement health maintenance, with emphasis on implications for regenerative and precision medicine. Methods: A systematic literature review was conducted using PubMed, Scopus, and Web of Science. Studies published up to 2025 were screened according to predefined inclusion criteria, focusing on peer-reviewed research examining AI applications in occupational health assessment, monitoring, and preventive care across different stages of the workforce lifecycle. Results: The reviewed studies indicate that AI can enhance pre-employment health screening through improved risk prediction and functional assessment. In routine occupational health monitoring, AI integrated with wearable technologies supports continuous physiological surveillance and early detection of health risks. For post-retirement populations, AI-driven biological aging models facilitate personalized health management and preventive intervention planning. Federated learning approaches also show promise for maintaining data privacy while enabling large-scale health analytics. However, current evidence remains constrained by limited prospective validation and heterogeneous study designsConclusions: AI-enabled MCU programs represent a critical frontier for precision occupational health, offering opportunities to improve risk assessment, disease prevention, and long-term workforce well-being. Nevertheless, challenges related to regulatory compliance, algorithmic fairness, data privacy, and clinical validation must be addressed before widespread implementation can be achieved.
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References
D. J. W. Stahl, J. P. Lee, and K. R. McDonald, "Does retirement offer a 'window of opportunity' for lifestyle change? Views from English workers on the cusp of retirement," Journal of Aging and Health, vol. 28, pp. 1061–1079, 2016. DOI: 10.1177/0898264315624903
E. Koundourakis, N. Andrianakis, and M. Linardakis, "Metabolic syndrome in retired soccer players: a pilot study," Obesity Medicine, vol. 8, pp. 1–6, 2017. DOI: 10.1016/j.obmed.2017.09.004
G. Lee, B. Choi, H. Jebelli, and S. Lee, "Assessment of construction workers' perceived risk using physiological data from wearable sensors: a machine learning approach," Journal of Building Engineering, vol. 42, 102824, 2021. DOI: 10.1016/j.jobe.2021.102824
J. A. Rincon, A. Costa, and G. Villarrubia, "Management of physical, mental and environmental stress at the workplace," 2017 International Conference on Intelligent Environments (IE), pp. 112–119, 2017. DOI: 10.1109/IE.2017.20
L. A. Meyers, K. J. Smith, and P. A. Roberts, "Including continuous glucose monitoring to provide personalized glycemic profiles as part of a pilot worksite health screening," Journal of Diabetes Science and Technology, vol. 14, pp. 1138–1145, 2020. DOI: 10.1177/1932296820966630
M. A. Bock, T. L. Schmidt, and R. J. Carter, "Test battery for assessment of cognitive function in older employees," Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2015. DOI: 10.1145/2769493.2769553
M. J. Rigby, "Ethical considerations of generative AI-enabled human resource management," Organizational Dynamics, vol. 53, 101032, 2024. DOI: 10.1016/j.orgdyn.2024.101032
P. K. Jebelli, S. Y. Choi, and H. Lee, "Heart rate modeling and prediction of construction workers based on physical activity using deep learning," Automation in Construction, vol. 155, 105077, 2023. DOI: 10.1016/j.autcon.2023.105077
S. J. H. McDonald, R. T. K. Ho, and C. Y. Wong, "Changes in physical activity during the retirement transition: a series of novel n-of-1 natural experiments," International Journal of Behavioral Nutrition and Physical Activity, vol. 14, 167, 2017. DOI: 10.1186/s12966-017-0623-7
S. S. Jain, P. K. Rajpurkar, and A. Y. Ng, "Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture," Cell Reports Medicine, vol. 5, 101481, 2024. DOI: 10.1016/j.xcrm.2024.101481
W. K. Mueller, B. K. Groves, and H. I. Park, "Measuring sedentary behavior by means of muscular activity and accelerometry," Sensors, vol. 18, 4010, 2018. DOI: 10.3390/s18114010
W. N. Price II and I. G. Cohen, "Privacy in the age of medical big data," Nature Medicine, vol. 25, pp. 37–43, 2019. DOI: 10.1038/s41591-018-0272-7
Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, "Dissecting racial bias in an algorithm used to manage the health of populations," Science, vol. 366, pp. 447–453, 2019. DOI: 10.1126/science.aax2342
Zhavoronkov and P. Mamoshina, "Deep aging clocks: the emergence of AI-based biomarkers of aging and longevity," Trends in Pharmacological Sciences, vol. 40, pp. 546–549, 2019. DOI: 10.1016/j.tips.2019.05.004
Zhavoronkov, E. Bischof, and K. F. Lee, "Artificial intelligence in longevity medicine," Nature Aging, vol. 1, pp. 5–7, 2021. DOI: 10.1038/s43587-020-00020-4