The Impact of Artificial Intelligence and Digitalization on the Georgian Healthcare Employment Market in 2027-2030
DOI:
https://doi.org/10.52340/ijch.2026.03.11Keywords:
health market, employment, artificial intelligence, digital migrationAbstract
The topic of our research is the employment market in Georgian healthcare with artificial intelligence and digital migration in mind. Artificial intelligence plus machine learning are fields of computer science in which you create systems that learn from data. Artificial intelligence and digital services are widely used in the healthcare sector, from automated daily operations to decision-making. The paper explains how these technologies are changing the employment market. Artificial intelligence and digitalization will drive the development of the healthcare industry. Technology will have a substantial impact on the Georgian medical labor market in 2027-2030. What place will physical actions, talent, and education have in the healthcare sector? Since artificial intelligence/machine learning has penetrated the healthcare industry, jobs are changing, and many will disappear while new ones are created. Healthcare, in particular, has been at the forefront of that technological progress, using AI/machine learning to expand remote healthcare services, medical diagnostics, and hospital systems.
The market for AI skills in healthcare is growing at 6 times the rate of any other sector. However, the introduction of AI/machine learning will require the digitization of work and the displacement of some employees. Today, the largest industry affected by the AI/machine learning revolution is healthcare. Hospitals and clinics are using them to improve their performance. The skills required by employers will change - more data analysis, programming, and analytical reasoning skills.
AI/machine learning will also automate many tasks, making many employees unemployed. Medical AI/Machine Learning Engineers, Data Scientists, and Algorithmic Engineers will replace many fields. To adapt to these changes, employees will need to develop a variety of skills, including technical expertise, creativity, and social intelligence.
The research that was conducted is of two types: expert opinion and literature review. The research indicates that we need to prepare to educate a new type of healthcare professional.
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References
Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2023). Addressing algorithmic bias and the perpetuation of health inequities: An AI bias-aware framework. Health Policy and Technology, 12(1), 100702.
Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization, 33(3), 100478.
Act, EAI (2024). The EU Artificial Intelligence Act. European Union.
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, AV, ... & Al-Muhanna, FA (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6), 951.
Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V. I., & Precise4Q Consortium. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC medical informatics and decision making, 20(1), 310.
Al-Antari, MA (2023). Artificial intelligence for medical diagnostics—Existing and future AI technology!. Diagnostics, 13(4), 688.
Alowais, SA, Alghamdi, SS, Alsuhebany, N., Alqahtani, T., Alshaya, AI, Almohareb, SN, ... & Albekairy, AM (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689.
Bekbolatova, M., Mayer, J., Ong, CW, & Toma, M. (2024, January). Transformative potential of AI in healthcare: definitions, applications, and navigating the ethical landscape and public perspectives. In Healthcare (Vol. 12, No. 2, p. 125). MDPI.
Bekbolatova, M., Mayer, J., Ong, CW, & Toma, M. (2024, January). Transformative potential of AI in healthcare: definitions, applications, and navigating the ethical landscape and public perspectives. In Healthcare (Vol. 12, No. 2, p. 125). MDPI.
Bouderhem, R. (2024). Shaping the future of AI in healthcare through ethics and governance. Humanities and social sciences communications, 11(1), 1-12.
Chinta, SV, Wang, Z., Palikhe, A., Zhang, X., Kashif, A., Smith, MA, ... & Zhang, W. (2025). AI-driven healthcare: Fairness in AI healthcare: A survey. PLOS Digital Health, 4(5).
Djakeli, K. (Ed.). (2023). Modern healthcare marketing in the digital era. IGI Global.
Djakeli, K. (2025). Phygitalization of Palliative Care and Its Social Marketing. In Phygital Approaches to Social Marketing (pp. 267-288). IGI Global Scientific Publishing.
Gigauri, I., & Djakeli, K. (2021). Expecting Transformation of Marketing During the Post-Pandemic New Normal: Qualitative Research of Marketing Managers in Georgia. International Journal of Sustainable Economies Management (IJSEM), 10(2), 1-18.
Madiega, T. (2021, April). Artificial Intelligence Act.
Gognadze, K. (2025). DIFFICULTIES INTRODUCING AND MANAGING ARTIFICIAL INTELLIGENCE IN THE GEORGIAN HEALTHCARE SYSTEM. Innovative economics and management, 12(1), 90-108.
Vepkhishvili, G., Kekelia, E., & Abuladze, M. (2025). 273P AI-assisted extraction and analysis of prostate cancer data in a low-resource European setting: A single-center experience from Georgia. ESMO Real World Data and Digital Oncology, 10.
Schuett, J. (2024). Risk management in the Artificial Intelligence Act. European Journal of Risk Regulation, 15(2), 367-385.
Giguashvili, G., Makasarashvili, T., & Mamatsashvili, D. (2023). Application OF Technologies Based on Artificial Intelligence in the Healthcare Sector OF Georgia. Grail of Science, (32), 366-370.
Novelli, C., Hacker, P., Morley, J., Trondal, J., & Floridi, L. (2025). A robust governance for the AI Act: AI office, AI board, scientific panel, and national authorities. European Journal of Risk Regulation, 16(2), 566-590.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4).
Shaheen, M. Y. (2021). Applications of Artificial Intelligence (AI) in healthcare: A review. ScienceOpen Preprints.
Koski, E., & Murphy, J. (2021). AI in Healthcare. Nurses and midwives in the digital age, 295-299.
Polevikov, S. (2023). Advancing AI in healthcare: a comprehensive review of best practices. Clinica Chimica Acta, 548, 117519.
Panch, T., Mattie, H., & Celi, LA (2019). The "inconvenient truth" about AI in healthcare. NPJ Digital Medicine, 2(1), 77.
Saraswat, D., Bhattacharya, P., Verma, A., Prasad, VK, Tanwar, S., Sharma, G., ... & Sharma, R. (2022). Explainable AI for healthcare 5.0: opportunities and challenges. IEEE Access, 10, 84486-84517.
Zeb, S., Nizamullah, FNU, Abbasi, N., & Fahad, M. (2024). AI in healthcare: revolutionizing diagnosis and therapy. International Journal of Multidisciplinary Sciences and Arts, 3(3), 118-128.
Talati, D. (2023). AI in the healthcare domain. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (Online), 2(3), 256-262.
Väänänen, A., Haataja, K., Vehviläinen-Julkunen, K., & Toivanen, P. (2021). AI in healthcare: A narrative review. F1000Research, 10, 6.
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497.
Sun, G., & Zhou, YH (2023). AI in healthcare: navigating opportunities and challenges in digital communication. Frontiers in Digital Health, 5, 1291132.
Morley, J., Machado, CC, Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2021). The ethics of AI in health care: a mapping review. Ethics, Governance, and Policies in Artificial Intelligence, 313-346.
Steerling, E., Siira, E., Nilsen, P., Svedberg, P., & Nygren, J. (2023). Implementing AI in healthcare—the relevance of trust: a scoping review. Frontiers in health services, 3, 1211150.
Li, F., Ruijs, N., & Lu, Y. (2022). Ethics & AI: A systematic review on ethical concerns and related strategies for designing with AI in healthcare. AI, 4(1), 28-53.
Ejjami, R. (2024). AI-driven healthcare in France. International Journal for Multidisciplinary Research.
Nasir, S., Khan, RA, & Bai, S. (2024). Ethical framework for harnessing the power of AI in healthcare and beyond. IEEE Access, 12, 31014-31035.
Muley, A., Muzumdar, P., Kurian, G., & Basyal, G. P. (2023). Risk of AI in healthcare: a comprehensive literature review and study framework. arXiv preprint arXiv:2309.14530.
Shaheen, M. Y. (2021). AI in Healthcare: medical and socio-economic benefits and challenges. ScienceOpen Preprints.
Kulkarni, PA, & Singh, H. (2023). Artificial intelligence in clinical diagnosis: opportunities, challenges, and hype. Journal, 330(4), 317-318.
Zeb, S., Nizamullah, FNU, Abbasi, N., & Fahad, M. (2024). AI in healthcare: revolutionizing diagnosis and therapy. International Journal of Multidisciplinary Sciences and Arts, 3(3), 118-128.
Norori, N., Hu, Q., Aellen, FM, Faraci, FD, & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(10).
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., ... & Naganawa, S. (2024). Fairness of artificial intelligence in healthcare: review and recommendations. Japanese journal of radiology, 42(1), 3-15.
Durán, JM, & Jongsma, KR (2021). Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. Journal of Medical Ethics, 47(5), 329-335.
Nouis, S. C., Uren, V., & Jariwala, S. (2025). Evaluating accountability, transparency, and bias in AI-assisted healthcare decision-making: a qualitative study of healthcare professionals' perspectives in the UK. BMC Medical Ethics, 26(1), 89.
Panahi, O. (2025). Algorithmic medicine. Journal of Medical Discoveries, 2(1).
George, AS, George, AH, & Baskar, T. (2024). Artificial intelligence and the future of healthcare: Emerging jobs and skills in 2035. Partners Universal Multidisciplinary Research Journal, 1(1), 1-21.
Srivastava, A. (2024). Impact of AI/ML on the job market, skill set, and the health industry. ESP J. Eng. Technol. Adv, 4(3), 122-126.
Knopp, MI, Warm, EJ, Weber, D., Kelleher, M., Kinnear, B., Schumacher, DJ, ... & Turner, L. (2023). AI-enabled medical education: threads of change, promising futures, and risky realities across four potential future worlds. JMIR Medical Education, 9, e50373.
Pang, TY, Lee, TK, & Murshed, M. (2023). Towards a new paradigm for digital health training and education in Australia: exploring the implications of the fifth industrial revolution. Applied Sciences, 13(11), 6854.
Obuchowicz, R., Lasek, J., Wodziński, M., Piórkowski, A., Strzelecki, M., & Nurzynska, K. (2025). Artificial intelligence-empowered radiology—current status and critical review. Diagnostics, 15(3), 282.
Gilbert, S., Anderson, S., Daumer, M., Li, P., Melvin, T., & Williams, R. (2023). Learning from experience and finding the right balance in the governance of artificial intelligence and digital health technologies. Journal of Medical Internet Research, 25(1), e43682.
KUTALIA, I., GIGINEISHVILI, K., & KATSITADZE, A. (2025). ARTIFICIAL INTELLIGENCE IN DERMATOLOGY: CURRENT STATE, CHALLENGES AND FUTURE PERSPECTIVES. Experimental and Clinical Medicine Georgia, (6), 167-170.
Djakeli, K. (2026). Healthcare Reforms in Georgia: Its Economy, Marketing, and Leadership. In Physical Insights on National Health Reform: Economics, Marketing, and Leadership (pp. 265-282). IGI Global Scientific Publishing.
Sopromadze, Z., Shashiashvili, N., & Simonishvili, B. (2025). Strategies for Overcoming Barriers to the Integration of Artificial Intelligence (AI) in Pharmaceutical Practice in Georgia. Economics & Business (1987-5789), (2).
Voronovi, S., Simonia, G., & Taylor, DC (2024). Medical education in Georgia.
