KaydedilenlerDerginizi ekleyin

Dergiler / Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Cilt: 35

SAĞLIK SEKTÖRÜNDE YAPAY ZEKÂ VE İSTİHDAM İLİŞKİSİ

THE RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE AND EMPLOYMENT IN THE HEALTHCARE SECTOR

Ayşe Tekin Balcı(MANİSA CELÂL BAYAR ÜNİVERSİTESİ)0000-0002-4318-0414
Rukiye Çakmak(YOZGAT BOZOK ÜNİVERSİTESİ)0000-0003-0088-710X
Sayfa
—
Yayın Tarihi
2026-05-18
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bu nedenle, gelecekte sağlık hizmetlerinde teknolojinin getirdiği fırsatlardan maksimum düzeyde yararlanmak için dengeli ve sürdürülebilir politikalar geliştirilmesi önemlidir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bilim ve teknolojideki hızlı ilerlemeler, sağlık sektöründe dijital dönüşümü hızlandırmış ve sağlık hizmetlerinin iyileştirilmesi, planlanması ve finansmanında önemli değişimlere yol açmıştır.
📊
Temel Bulgular & Veri:Yapay zekâ, sağlık hizmetlerinin iyileştirilmesi, hasta bakım kalitesinin arttırılması, maliyetlerin düşürülmesi, tıbbi karar verme, erken tanı, ilaç geliştirme ve politikalarının planlanması gibi alanlarda yaygın olarak kullanılmaktadır Sağlık alanında yaşanan dijital dönüşüm istihdam üzerinde de önemli etkilere sahiptir.
💡
Sonuç & Katkı:Bu nedenle, gelecekte sağlık hizmetlerinde teknolojinin getirdiği fırsatlardan maksimum düzeyde yararlanmak için dengeli ve sürdürülebilir politikalar geliştirilmesi önemlidir.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Sağlık · Sağlık Teknolojileri · Yapay Zeka

Özet

Bilim ve teknolojideki hızlı ilerlemeler, sağlık sektöründe dijital dönüşümü hızlandırmış ve sağlık hizmetlerinin iyileştirilmesi, planlanması ve finansmanında önemli değişimlere yol açmıştır. Yapay zekâ, bu dijitalleşme sürecinin doğal bir sonucu olarak karşımıza çıkmaktadır. Yapay zekâ, sağlık hizmetlerinin iyileştirilmesi, hasta bakım kalitesinin arttırılması, maliyetlerin düşürülmesi, tıbbi karar verme, erken tanı, ilaç geliştirme ve politikalarının planlanması gibi alanlarda yaygın olarak kullanılmaktadır Sağlık alanında yaşanan dijital dönüşüm istihdam üzerinde de önemli etkilere sahiptir. Ancak, sağlık alanında dijitalleşmenin ilerlemesiyle birlikte, istihdam dinamiklerinde de önemli değişiklikler meydana gelmektedir. Bu çalışma, sağlık sektöründe yaşanan dijital dönüşümün etkilerini ele almakta; yapay zekâ teknolojilerinin sağlık alanında ortaya çıkardığı olumlu gelişmeleri ve sağlık istihdamında meydana gelmesi muhtemel dönüşümleri incelemektedir. Bu doğrultuda, sağlık sektöründe kullanılan yapay zekâ teknolojilerinin istihdam üzerindeki etkileri, literatür taraması yöntemi kullanılarak değerlendirilmektedir. Bu kapsamda yapılan literatür taraması sonucunda yapay zeka teknolojilerinin yaygınlaşması sağlık maliyetlerini düşürebileceği gibi, iş gücü piyasasında dengesizlikler ve ekonomik riskler de yaratabilir. Bu nedenle, gelecekte sağlık hizmetlerinde teknolojinin getirdiği fırsatlardan maksimum düzeyde yararlanmak için dengeli ve sürdürülebilir politikalar geliştirilmesi önemlidir.

Abstract

Rapid advances in science and technology have accelerated digital transformation in the healthcare sector leading to significant changes in the delivery, planning and financing of healthcare services. Artificial intelligence is a natural consequence of this digitalisation process. Artificial intelligence is widely used in areas such as improving healthcare services, increasing the quality of patient care, reducing costs, supporting medical decision-making, early diagnosis, facilitating drug development and contributing to policy planning. However, with the advancement of digitalisation in the field of healthcare, significant changes in employment dynamics also occur. This study examines the effects of the digital transformation taking place in the healthcare sector, focusing on the positive developments brought about by artificial intelligence technologies and the potential transformations in healthcare employment. In this context, the impacts of artificial intelligence technologies used in the healthcare sector on employment are evaluated through a comprehensive review of the existing literature. As a result of the literature review conducted in this context, the proliferation of artificial intelligence technologies may reduce health costs, but may also create imbalances and economic risks in the labour market. Therefore, it is important to develop balanced and sustainable policies to maximise the opportunities brought by technology in health services in the future.

🔖

Bu Makaleye Atıf Yap

Balcı, A. T., Çakmak, R. (2026). SAĞLIK SEKTÖRÜNDE YAPAY ZEKÂ VE İSTİHDAM İLİŞKİSİ. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 35. https://doi.org/10.35379/cusosbil.1652653

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (73)

  1. AI in healthcare: How AI can enhance the patients’ experience in healthcare. PricewaterhouseCoopers. https://www.pwc.com/us/en/industries/health-industries/library/ai-in-healthcare.html
  2. Akalın, B., & Veranyurt, Ü. (2020). Digitalisation and artificial intelligence in health. Journal of Health Management, 2(2), 131–141.
  3. Alacadağlı, E. (2019). Knowledge management, digitalisation and Turkish health system. Electronic Turkish Studies, 14(2), 67–86.
  4. Aslan, Ş., & Güzel, Ş. (2019). Industry 4.0 development process and digital transformation in health. In Proceedings of the 2nd International Congress on New Horizons in Education and Social Sciences (ICES-2019).
  5. Ataman, B. C. (1998). Technological Unemployment and the Labor Market in Turkey. Ankara: Publications of the Confederation of Turkish Employer Associations.
  6. Aung, Y.Y.M, Wong, D.C.S, & Ting D.S.W. (2021). The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 139(1), 4-15. doi: https://doi.org/10.1093/bmb/ldab016
  7. Autor, D. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30.
  8. Azzi, S., Gagnon, S., Ramirez, A., & Richards, G. (2020). Healthcare applications of artificial intelligence and analytics: A review and proposed framework. Applied Sciences, 10(18), 6553. https://doi.org/10.3390/app10186553
  9. Bacaksız, F. E., Yılmaz, M., Ezizi, K., & Alan, H. (2020). Managing Robots in Healthcare Services. Journal of Health and Nursing Management. 7(3), 458–465. https://doi.org/10.5222/SHYD.2020.59455.
  10. Bhavnani, S. P., Parakh, K., Atreja, A., Druz, R., Graham, G. N., Hayek, S. S., Krumholz, H. M., Maddox, T. M., Majmudar, M. D., Rumsfeld, J. S., & Shah, B. R. (2017). Roadmap for innovation—ACC health policy statement on healthcare transformation in the era of digital health, big data, and precision health. Journal of the American College of Cardiology, 70(21), 2696–2718.
  11. Bilge, U. (2007). Artificial intelligence and expert systems in medicine. In Proceedings of the 4th Medical Informatics Congress.
  12. Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms. Upturn.
  13. Büyükgöze, S., & Dereli, E. (2019). Artificial intelligence in digital health applications. In Proceedings of the 6th International Congress of Scientific and Professional Studies: Science and Health.
  14. Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.
  15. Delanerolle, G., Yang, X., Shetty, S., Raymont, V., Shetty, A., Phiri, P., Hapangama, D. K., Tempest, N., Majumder, K., & Shi, J. Q. (2021). Artificial intelligence: A rapid case for advancement in the personalization of gynecology/obstetric and mental health care. Women’s Health. https://doi.org/10.1177/17455065211018111
  16. Demirci, Ş. (2019). Digitalization of health. Mehmet Akif Ersoy University Journal of Social Sciences Institute, 10(26), 710–721. https://doi.org/10.20875/makusobed.383071
  17. Demirhan, A., Kılıç, Y. A., & Güler, İ. (2010). Artificial intelligence applications in medicine. Journal of Intensive Care, 9(1), 31–41.
  18. Diri, F. (2024). Assessment of artificial intelligence technology and its implications in the context of Turkish health law. Journal of Information Technology Law, 6(1), 270–320.
  19. Durning, M. V. (2012). Automated breast ultrasound far faster than hand-held. Diagnostic Imaging. http://www.diagnosticimaging.com/articles/automated-breast-ultrasound-far-faster-hand-held.
  20. Erebak, S. (2020). The Transformation of Human Competencies Required in Care Services for Older Adults in the Context of Gerontechnology.” Current Research in Economics and Administrative Sciences, 816–833.
  21. Ford, M. (2021). Rise of the Robots: Technology and the Threat of a Jobless Future” (Trans. C. Duran). Kronik Kitap
  22. Gavrilov, G., Simov, O., & Trajkovik, V. (2020). Analysis of digitalization in healthcare: Case study. In International Conference on ICT Innovations (pp. 202–216). Springer.
  23. Glauner, P. (2021). Artificial intelligence in healthcare: Foundations, opportunities, and challenges. In P. Glauner, P. Plugmann, & G. Lerzynski (Eds.), Digitalization in healthcare. Springer. https://doi.org/10.1007/978-3-030-65896-0_1
  24. Gücenmez, T. (2023). The impact of digital transformation on labor markets and income distribution in Turkey. Kahramanmaraş Sütçü İmam University Journal of Social Sciences, 20(3), 995–1005. https://doi.org/10.33437/ksusbd.1361092
  25. Hashiguchi, T. C. O., Oderkirk, J., & Slawomirski, L. (2022). Fulfilling the promise of artificial intelligence in the health sector: Let’s get real. Value in Health, 25(3), 368–373. https://doi.org/10.1016/j.jval.2021.11.1369
  26. Hellmann, A., Emmons, A., Stewart Prime, M., Paranjape, K., & Heaney, D. L. (2023). Digital health: Today’s solutions and tomorrow’s impact. Clinical Laboratory Medicine, 43(1), 71–86.
  27. Hochmuth, A., Wrona, K. J., Exner, A. K., & Dockweiler, C. (2021). Digitization and health inequality and equity in nursing. Pflege, 34(3), 151–158.
  28. Holland, J., Kingston, L., McCarthy, C., Armstrong, E., O’Dwyer, P., Merz, F., & McConnell, M. (2021). Service robots in the healthcare sector. Robotics, 10(1), 47.
  29. Hudes, M. K. (2017). Fostering innovation in digital health: A new ecosystem. In Proceedings of the Microelectronics Symposium (Pan Pacific). https://doi.org/10.37665/ppRPVMF16010
  30. IBM. (2024). Watson. https://www.ibm.com/watson.
  31. Institute of Medicine (US). (2007). Computational technology for effective health care: Immediate steps and strategic directions. National Academies Press. https://doi.org/10.17226/11925.
  32. Jarrahi, M. H. (2018). Artificial intelligence and the future of work. Business Horizons, 61(4), 577–586.
  33. Kaynar, M. (2024). Artificial intelligence, artificial intelligence ethics, and the future of human nature (Master’s thesis, Istanbul Sabahattin Zaim University).
  34. Kılıç, M. E., & Kılıç, K. D. (2022). Contractual liability in artificial intelligence-assisted reproductive treatment practices. Journal of Medical Law, 22, 411–452.
  35. Kılıç, T. ve Tosun, N. (2021). Smart Healthcare Ecosystem and Current Application Examples.” Journal of Business Science, 9(3), 543–564.
  36. King, R. (2010). Soon, that nearby worker might be a robot. Bloomberg Businessweek. http://www.businessweek.com/stories/2010/06/02/soon-that-nearby-worker-might-be-a-robot
  37. Koca, D. (2020). Sanayi devrimi, teknolojik işsizlik ve emek piyasasına etkileri. Ankara: Nobel Akademik Yayıncılık.
  38. Kukhtevich, I. I., Goryunova, T. I., Goryunova, V. V., & Zhilyaev, P. S. (2020). Digitalization in healthcare and telecommunication support systems in medicine. Advances in Economics, Business and Management Research, 148, 364–369.
  39. Lee, K. Y., & Kim, J. (2016). Artificial intelligence technology trends and IBM Watson references in the medical field. Korean medical education review, 18(2), 51-57.
  40. Malerbi, F. K., Nakayama, L. F., Gayle Dychiao, R., Zago Ribeiro, L., Villanueva, C., Celi, L. A., & Regatieri, C. V. (2023). Digital education for the deployment of artificial intelligence in health care. Journal of Medical Internet Research, 25, e43333.
  41. Manjoo, F. (2011). Why the highest-paid doctors are the most vulnerable to automation. Slate. https://www.slate.com/articles/technology/robot_invasion/2011/09/will_robots_steal_your_job_3.html.
  42. Mankiw, N. G. (2019). Macroeconomics (10th ed.). Worth Publishers.
  43. Mayol, J. (2024). Digital solutions and health sciences. Cirugía Española (English Edition), 102, 3–7. https://doi.org/10.1016/j.cireng.2023.11.011
  44. Meskó, B., & Görög, M. (2020). A short guide for medical professionals in the era of artificial intelligence. NPJ Digital Medicine, 3(1), 126. https://doi.org/10.1038/s41746-020-00333-z
  45. Mesko, B., Drobni, Z., Benyei, E., Gergely, B., & Győrffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. mHealth, 3(1). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5682364/
  46. Nilsson, N. J. (1998). Artificial intelligence: A new synthesis. Morgan Kaufmann.
  47. OECD. (2019). Artificial intelligence and the future of skills. OECD Publishing.
  48. Park, C. W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., Chang, D. K., Kim, H., Kim, H., Lee, H., Jang, J., Ye, J. C., Jeon, J. H., Seo, J. B., Kim, K. J., Jung, K. H., Kim, N., Paek, S., Shin, S. Y., Yoo, S., Choi, Y. S., Kim, Y., & Yoon, H. J. (2020). Artificial intelligence in health care: Current applications and issues. Journal of Korean Medical Science, 35(42), e379. https://doi.org/10.3346/jkms.2020.35.e37
  49. Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253.
  50. Peyrou, B., Vignaux, J. J., & André, A. (2018). Artificial intelligence and healthcare. In Digital health (pp. 29–40).
  51. Rahman, M. A., Victoros, E., Ernest, J., Davis, R., Shanjana, Y., & Islam, M. R. (2024). Impact of artificial intelligence (AI) technology in healthcare sector: A critical evaluation of both sides of the coin. Clinical Pathology, 17.
  52. Ramesh, A. N., Kambhampati, C., Monson, J. R., & Drew, P. J. (2004). Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England, 86(5), 334–338. https://doi.org/10.1308/147870804290
  53. Rani, S., Chauhan, M., Kataria, A., & Khang, A. (2021). IoT equipped intelligent distributed framework for smart healthcare systems. In Towards the integration of IoT, cloud and big data: Services, applications and standards (pp. 97–114). Springer.
  54. Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510
  55. Rigla, M., García-Sáez, G., Pons, B., & Hernando, M. E. (2018). Artificial intelligence methodologies and their application to diabetes. Journal of Diabetes Science and Technology, 12(2), 303–310. https://doi.org/10.1177/1932296817710475
  56. Rouhiainen, L. (2020). Artificial Intelligence: 101 Things You Must Know Today About Our Future. Lasse Rouhiainen Publishing.
  57. Schijven, M. P., & Kroh, M. (2023). Editorial: Harnessing the power of AI in health care: Benefits, risks, and preparation. Surgical Innovation, 30(4), 417–418. https://doi.org/10.1177/15533506231190748
  58. Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. The Lancet, 395(10236), 1579–1586.
  59. Stark, R. (2011). The looming doctor shortage. Washington Policy Center. http://www.washingtonpolicy.org/publications/notes/looming-doctor-shortage
  60. Stasevych, M., & Zvarych, V. (2023). Innovative robotic technologies and artificial intelligence in pharmacy and medicine: Paving the way for the future of health care—a review. Big Data and Cognitive Computing, 7(3), 147. https://doi.org/10.3390/bdcc7030147
  61. Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2020). The influence of artificial intelligence on human resource management. Human Resource Management Review, 25, 216–231. https://doi.org/10.1016/j.hrmr.2015.01.002
  62. Stoumpos, A. I., Kitsios, F., & Talias, M. A. (2023). Digital transformation in healthcare: Technology acceptance and its applications. International Journal of Environmental Research and Public Health, 20(4), 3407. https://doi.org/10.3390/ijerph20043407
  63. Şahinol, M., & Başkavak, G. (2020). Biomedicalization in Turkey: Digitalization of health and self-tracking practices. In STS in Turkey: Introduction to Science and Technology Studies (pp. 103–117).
  64. Şimşir, İ., & Mete, B. (2021). The future of healthcare: Digital health technologies. Journal of Innovative Healthcare Practices, 2(1), 33–39.
  65. The Economist. (2013, May). The caring robot. http://www.economist.com/blogs/babbage/2013/05/automation-elderly.
  66. The Population Project. (2024). World population prospects 2024: Summary of results. Population.UN.org. https://population.un.org.
  67. Topol, E. (2019). Deep medicine. Basic Books.
  68. Uzun, T. (2020). Artificial intelligence and health practices. İzmir Katip Çelebi University Journal of Faculty of Economics and Administrative Sciences, 2(1), 80–92.
  69. Wikipedia. (2024). Neo-Luddism. In Wikipedia. https://tr.wikipedia.org/wiki/Neo-Luddizm
  70. World Economic Forum. (2020). The future of jobs report.
  71. Yılmaz, K., & Özdağoğlu, A. (2020). Awareness analysis of industry 4.0. Mehmet Akif Ersoy University Journal of Institute of Social Sciences, 31, 7–25. https://doi.org/10.20875/makusobed.459431
  72. Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2, 719–731.
  73. Yücel, G., & Adiloğlu, B. (2019). Digitalization, artificial intelligence and accounting expectations. Journal of Accounting and Finance History Research, 17, 47–60.