Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment

Mehmed Zahid Çögenli; Fatih Ulukaya

doi:10.54733/smar.2020406

EN TR

Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment

Abstract

Accessible multimodal AI assistants can produce immediate workplace risk judgments from ordinary images, raising questions about when organizations may treat automated outputs as substitutes for professional expertise. This study examined how first-response ergonomic risk judgments from ChatGPT, Claude, and Gemini, accessed in July 2026, aligned with fixed expert Rapid Entire Body Assessment (REBA) references within an expert-validated medium-risk dental workplace benchmark. Twelve de-identified working-posture images were submitted once to each assistant using the same standardized Turkish prompt. Two experts independently scored each image without AI outputs, resolved differences by discussion, and then evaluated model-blinded responses against the agreed REBA references. Blinded expert calibration was the principal inferential outcome; direct categorical calibration and two critical-error indicators were supporting analyses. All three assistants identified visible postural concerns, but their severity judgments differed within the same reference distribution. ChatGPT usually retained the expert-supported medium-risk category, whereas Claude and Gemini frequently shifted the same cases upward. The experts showed strong agreement in identifying well-calibrated, incorrectly calibrated, and excessively certain responses. The findings show that successful observation does not by itself establish reliable classification. We therefore frame risk calibration as a boundary condition for bounded substitution: Competence at one stage of professional work does not automatically justify delegating authority over subsequent classification or action. These results are time-bounded to the tested July 2026 model versions and do not establish performance across the full REBA spectrum.

Keywords

multimodal artificial intelligence (AI), workplace ergonomics, risk calibration, professional judgment, organizational governance, occupational health and safety management

İşyeri Ergonomik Risk Değerlendirmesinde Çok Modlu Yapay Zekâ ve Uzman Yargısı

Abstract

Erişilebilir çok modlu yapay zekâ asistanları sıradan görsellerden anlık işyeri risk yargıları üretebilmekte; bu durum, kuruluşların otomatik çıktıları hangi koşullarda mesleki uzmanlığın yerine kullanabileceği sorusunu gündeme getirmektedir. Bu çalışma, Temmuz 2026’da erişilen ChatGPT, Claude ve Gemini’nin ilk ergonomik risk yargılarının, uzmanlarca doğrulanmış orta risk düzeyindeki bir diş hekimliği işyeri kıyaslama setinde sabit uzman Hızlı Tüm Vücut Değerlendirmesi (REBA) referanslarıyla ne ölçüde uyum gösterdiğini incelemiştir. Kimliksizleştirilmiş on iki çalışma duruşu görseli, standartlaştırılmış Türkçe komutla her asistana bir kez sunulmuştur. İki uzman, yapay zekâ çıktılarına erişmeden görselleri bağımsız olarak puanlamış, farklılıkları tartışarak nihai REBA referanslarında uzlaşmış ve ardından model kimlikleri gizlenmiş yanıtları bu referanslara göre değerlendirmiştir. Körlenmiş uzman kalibrasyonu temel çıkarımsal sonuç olarak ele alınmış; doğrudan kategorik kalibrasyon ile iki kritik hata göstergesi destekleyici analizler olarak kullanılmıştır. Üç asistan da görünür duruş sorunlarını belirlemiş, ancak aynı referans dağılımı içindeki risk şiddeti yargıları farklılaşmıştır. ChatGPT çoğunlukla uzmanlar tarafından desteklenen orta risk kategorisini korurken Claude ve Gemini aynı vakaları sıklıkla daha yüksek kategorilere taşımıştır. Uzmanlar, iyi kalibre edilmiş, hatalı kalibre edilmiş ve aşırı kesinlik içeren yanıtları belirlemede güçlü uyum göstermiştir. Bulgular, başarılı gözlemin tek başına güvenilir sınıflandırmayı göstermediğini ortaya koymaktadır. Bu nedenle risk kalibrasyonu, sınırlı ikamenin (bounded substitution) bir sınır koşulu olarak ele alınmaktadır: Mesleki çalışmanın bir aşamasındaki yeterlilik, sonraki sınıflandırma veya eylem yetkisinin devredilmesini otomatik olarak haklı çıkarmaz. Sonuçlar, Temmuz 2026’da test edilen model sürümleriyle sınırlıdır ve REBA risk spektrumunun tamamındaki performansı ortaya koymamaktadır.

Keywords

çok modlu yapay zekâ, işyeri ergonomisi, risk kalibrasyonu, mesleki yargı, örgütsel yönetişim, iş sağlığı ve güvenliği yönetimi

References

Abbott, A. (1988). The system of professions: An essay on the division of expert labor. University of Chicago Press. https://doi.org/10.7208/chicago/9780226189666.001.0001
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30. https://doi.org/10.1257/jep.33.2.3
Al, B. (2025). The relationship between STARA awareness, psychological empowerment and organizational identification in the tourism sector: A quantitative research. Sosyal Mucit Academic Review, 6(Global Academic Perspective in Social Sciences), 147-175. https://doi.org/10.54733/smar.1768476
Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30. https://doi.org/10.1257/jep.29.3.3
Badri, A., Boudreau-Trudel, B., & Souissi, A. S. (2018). Occupational health and safety in the Industry 4.0 era: A cause for major concern? Safety Science, 109, 403–411. https://doi.org/10.1016/j.ssci.2018.06.012
Beane, M. (2019). Shadow learning: Building robotic surgical skill when approved means fail. Administrative Science Quarterly, 64(1), 87-123. https://doi.org/10.1177/0001839217751692
Blume, K. S., Holzgreve, F., Fraeulin, L., Erbe, C., Betz, W., Wanke, E. M., Brueggmann, D., Nienhaus, A., Maurer-Grubinger, C., Groneberg, D. A., & Ohlendorf, D. (2021). Ergonomic risk assessment of dental students-RULA applied to objective kinematic data. International Journal of Environmental Research and Public Health, 18(19), 10550. https://doi.org/10.3390/ijerph181910550
Braverman, H. (1974). Labor and monopoly capital: The degradation of work in the twentieth century. Monthly Review Press.
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257. https://doi.org/10.1017/jmo.2016.55
Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517-518. https://doi.org/10.1001/jama.2017.7797

Çögenli, M. Z. (2026). Visible AI assistant outputs in psychosocial risk management: An OHP/OHS-grounded benchmark for governance and responsible use. Frontiers in Public Health, 14, 1857113. https://doi.org/10.3389/fpubh.2026.1857113
Danylak, S., Walsh, L. J., & Zafar, S. (2024). Measuring ergonomic interventions and prevention programs for reducing musculoskeletal injury risk in the dental workforce: A systematic review. Journal of Dental Education, 88(2), 128-141. https://doi.org/10.1002/jdd.13403
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637-643. https://doi.org/10.1007/s12599-019-00595-2
Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62-70. https://doi.org/10.1016/j.infoandorg.2018.02.005
Freidson, E. (2001). Professionalism, the third logic: On the practice of knowledge. University of Chicago Press.
Gedikli, E. (2025). Sosyal dışlanma riski açısından sağlığın dijital belirleyicilerinin değerlendirilmesi. Sosyal Mucit Academic Review, 6(Global Academic Perspective in Social Sciences), 176-194. https://doi.org/10.54733/smar.1759330
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
Hasle, P., & Limborg, H. J. (2006). A review of the literature on preventive occupational health and safety activities in small enterprises. Industrial Health, 44(1), 6-12. https://doi.org/10.2486/indhealth.44.6
Hayes, M. J., Cockrell, D., & Smith, D. R. (2009). A systematic review of musculoskeletal disorders among dental professionals. International Journal of Dental Hygiene, 7(3), 159-165. https://doi.org/10.1111/j.1601-5037.2009.00395.x
Hignett, S., & McAtamney, L. (2000). Rapid Entire Body Assessment (REBA). Applied Ergonomics, 31(2), 201-205. https://doi.org/10.1016/S0003-6870(99)00039-3
Holzgreve, F., Fraeulin, L., Betz, W., Erbe, C., Wanke, E. M., Brüggmann, D., Nienhaus, A., Groneberg, D. A., Maurer-Grubinger, C., & Ohlendorf, D. (2022). A RULA-based comparison of the ergonomic risk of typical working procedures for dentists and dental assistants of general dentistry, endodontology, oral and maxillofacial surgery, and orthodontics. Sensors, 22(3), 805. https://doi.org/10.3390/s22030805
Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917-926. https://doi.org/10.1002/ajim.23037
Jahanimoghadam, F., Horri, A., Hasheminejad, N., Hashemi Nejad, N., & Baneshi, M. R. (2018). Ergonomic evaluation of dental professionals as determined by Rapid Entire Body Assessment method in 2014. Journal of Dentistry (Shiraz), 19(2), 155-158.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
Kaczmarczyk, R., Wilhelm, T. I., Martin, R., & Roos, J. (2024). Evaluating multimodal AI in medical diagnostics. npj Digital Medicine, 7, 205. https://doi.org/10.1038/s41746-024-01208-3
Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515–526. https://doi.org/10.1037/a0016755
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410. https://doi.org/10.5465/annals.2018.0174
Khanagar, S. B., Alshehri, A., Albalawi, F., Kalagi, S., Alghilan, M. A., Awawdeh, M., & Iyer, K. (2025). Development and performance of an artificial intelligence-based deep learning model designed for evaluating dental ergonomics. Healthcare, 13(18), 2277. https://doi.org/10.3390/healthcare13182277
Khanagar, S. B., Alshehri, A., Albalawi, F., Kalagi, S., Alghilan, M. A., Awawdeh, M., & Iyer, K. (2026). Evaluation of ergonomic work postures among dental students during restorative procedures using an artificial intelligence-based assessment model. Frontiers in Oral Health, 7, 1790719. https://doi.org/10.3389/froh.2026.1790719
Lee, H., Yoo, S., Kim, J., Cho, Y., Suh, D., & Lee, K. (2025). Comparative diagnostic performance of a multimodal large language model versus a dedicated electrocardiogram AI in detecting myocardial infarction from electrocardiogram images: Comparative study. JMIR AI, 4, e75910. https://doi.org/10.2196/75910
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80. https://doi.org/10.1518/hfes.46.1.50_30392
Lietz, J., Ulusoy, N., & Nienhaus, A. (2020). Prevention of musculoskeletal diseases and pain among dental professionals through ergonomic interventions: A systematic literature review. International Journal of Environmental Research and Public Health, 17(10), 3482. https://doi.org/10.3390/ijerph17103482
Mai, H.-N., Lee, D.-H., Kaenploy, J., Kim, J.-E., & Cho, S.-H. (2026). Performance of multimodal generative AI models in addressing complex dental inquiries with text, images, and analytical data. Journal of Esthetic and Restorative Dentistry, 38(1), 166-172. https://doi.org/10.1111/jerd.70064
Manohar, B. A., Devaraj, J., Maheswaran, C., & Pugalenthi, S. (2025). Performance evaluation of Rapid Entire Body Assessment using AI-assisted ergonomic analysis in dentistry. Biomimetics, 10(4), 239. https://doi.org/10.3390/biomimetics10040239
Mulimani, P., Hoe, V. C. W., Hayes, M. J., Idiculla, J. J., Abas, A. B. L., & Karanth, L. (2018). Ergonomic interventions for preventing musculoskeletal disorders in dental care practitioners. Cochrane Database of Systematic Reviews, 2018(10), CD011261. https://doi.org/10.1002/14651858.CD011261.pub2
Nam, Y., Kim, D. Y., Kyung, S., Seo, J., Song, J. M., Kwon, J., Kim, J., Jo, W., Park, H., Sung, J., Park, S., Kwon, H., Kwon, T., Kim, K., & Kim, N. (2025). Multimodal large language models in medical imaging: Current state and future directions. Korean Journal of Radiology, 26(10), 900-923. https://doi.org/10.3348/kjr.2025.0599
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230-253. https://doi.org/10.1518/001872097778543886
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 30(3), 286-297. https://doi.org/10.1109/3468.844354
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210. https://doi.org/10.5465/amr.2018.0072
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33-44). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873
Raman, V., Ramlogan, S., Sweet, J., & Sweet, D. (2020). Application of the Rapid Entire Body Assessment (REBA) in assessing chairside ergonomic risk of dental students. British Dental Journal. Advance online publication. https://doi.org/10.1038/s41415-020-1855-5
Robson, L. S., Clarke, J. A., Cullen, K., Bielecky, A., Severin, C., Bigelow, P. L., Irvin, E., Culyer, A., & Mahood, Q. (2007). The effectiveness of occupational health and safety management system interventions: A systematic review. Safety Science, 45(3), 329-353. https://doi.org/10.1016/j.ssci.2006.07.003
Savage, T., Wang, J., Gallo, R., Boukil, A., Patel, V., Safavi-Naini, S. A. A., Soroush, A., & Chen, J. H. (2025). Large language model uncertainty proxies: Discrimination and calibration for medical diagnosis and treatment. Journal of the American Medical Informatics Association, 32(1), 139-149. https://doi.org/10.1093/jamia/ocae254
Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57(2), 103174. https://doi.org/10.1016/j.im.2019.103174
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human-Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
Soo, S. Y., Ang, W. S., Chong, C. H., Tew, I. M., & Yahya, N. A. (2023). Occupational ergonomics and related musculoskeletal disorders among dentists: A systematic review. Work, 74(2), 469-476. https://doi.org/10.3233/WOR-211094
Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (pp. 1-9). Association for Computing Machinery. https://doi.org/10.1145/3465416.3483305
Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford University Press. https://doi.org/10.1093/oso/9780198713395.001.0001
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619867910
Yeniaras, V., & Altıniğne, N. (2023). Techno-insecurity, emotional exhaustion and job performance: A recommended theoretical framework. Sosyal Mucit Academic Review, 4(3), 410-433. https://doi.org/10.54733/smar.1314699

Details

Primary Language

English

Subjects

Organisational Behaviour

Journal Section

Research Article

Authors

Mehmed Zahid Çögenli ^*
0000-0003-3018-4157
Türkiye

Fatih Ulukaya
0000-0001-9285-668X
Türkiye

Publication Date

October 30, 2026

Submission Date

August 17, 2026

Acceptance Date

October 2, 2026

Published in Issue

Year 2026 Volume: 7 Number: Global Academic Issues in Social Science

DOI

https://doi.org/10.54733/smar.2020406

IZ

https://izlik.org/JA63AG54RE

APA

Çögenli, M. Z., & Ulukaya, F. (2026). Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment. Sosyal Mucit Academic Review, 7(Global Academic Issues in Social Science), 63-86. https://doi.org/10.54733/smar.2020406

AMA

1.Çögenli MZ, Ulukaya F. Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment. SMAR. 2026;7(Global Academic Issues in Social Science):63-86. doi:10.54733/smar.2020406

Chicago

Çögenli, Mehmed Zahid, and Fatih Ulukaya. 2026. “Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment”. Sosyal Mucit Academic Review 7 (Global Academic Issues in Social Science): 63-86. https://doi.org/10.54733/smar.2020406.

EndNote

Çögenli MZ, Ulukaya F (October 1, 2026) Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment. Sosyal Mucit Academic Review 7 Global Academic Issues in Social Science 63–86.

IEEE

[1]M. Z. Çögenli and F. Ulukaya, “Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment”, SMAR, vol. 7, no. Global Academic Issues in Social Science, pp. 63–86, Oct. 2026, doi: 10.54733/smar.2020406.

ISNAD

Çögenli, Mehmed Zahid - Ulukaya, Fatih. “Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment”. Sosyal Mucit Academic Review 7/Global Academic Issues in Social Science (October 1, 2026): 63-86. https://doi.org/10.54733/smar.2020406.

JAMA

1.Çögenli MZ, Ulukaya F. Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment. SMAR. 2026;7:63–86.

MLA

Çögenli, Mehmed Zahid, and Fatih Ulukaya. “Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment”. Sosyal Mucit Academic Review, vol. 7, no. Global Academic Issues in Social Science, Oct. 2026, pp. 63-86, doi:10.54733/smar.2020406.

Vancouver

1.Mehmed Zahid Çögenli, Fatih Ulukaya. Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment. SMAR. 2026 Oct. 1;7(Global Academic Issues in Social Science):63-86. doi:10.54733/smar.2020406

Articles published in Sosyal Mucit Academic Review are licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Multimodal AI and Expert Judgment in Workplace Ergonomic Risk Assessment

Abstract

Keywords

İşyeri Ergonomik Risk Değerlendirmesinde Çok Modlu Yapay Zekâ ve Uzman Yargısı

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite