{"id":35569,"date":"2025-12-14T13:40:49","date_gmt":"2025-12-14T09:40:49","guid":{"rendered":"https:\/\/cue.edu.ge\/?post_type=articles&p=35569"},"modified":"2025-12-14T13:42:00","modified_gmt":"2025-12-14T09:42:00","slug":"ai-in-medical-education","status":"publish","type":"articles","link":"https:\/\/cue.edu.ge\/en\/articles\/ai-in-medical-education\/","title":{"rendered":"AI in Medical Education"},"content":{"rendered":"

Tamari Pertaia<\/strong><\/p>\n

Dean of the Medical Faculty, Central University of Europe<\/p>\n

tamar.pertaia@cue.edu.ge<\/a><\/p>\n

\u00a0<\/strong><\/p>\n

Abstract<\/strong><\/p>\n

Artificial Intelligence (AI) has emerged as a transformative force in medical education, offering solutions to the longstanding limitations of traditional training. This article explores AI\u2019s role across five critical domains: clinical training, virtual patient simulation, personalized learning, curriculum integration, and assessment. The COVID-19 pandemic underscored the urgency of flexible learning environments, and AI-based platforms such as Osso VR, Touch Surgery, Body Interact, Smart Sparrow, and Virti illustrate the capacity of intelligent systems to maintain continuity, improve engagement, and benchmark outcomes globally and regionally.<\/p>\n

In clinical training, AI-powered simulations enhance surgical and procedural practice through immersive, risk-free environments, significantly improving accuracy, retention, and confidence. Virtual patient systems further extend exposure to rare and critical cases, fostering diagnostic reasoning, interprofessional communication, and adaptive decision-making. Personalized learning, enabled by adaptive algorithms, addresses individual needs, ensuring mastery through predictive analytics and targeted remediation. Evidence indicates improved Objective Structured Clinical Examination (OSCE) performance and reduced diagnostic errors when students are guided by AI-supported platforms. Curriculum integration is another key theme: AI modules in radiology, pathology, and internal medicine allow benchmarking against global standards, while simultaneously supporting accreditation requirements. This is particularly salient in Georgia, where medical schools are piloting AI assessment tools to align competencies with international benchmarks. The role of AI in assessment is equally transformative, ranging from automated grading with Natural Language Processing (NLP) to AI-assisted OSCE evaluations and image-based diagnostic testing, ensuring objectivity, scalability, and timeliness. The pandemic context revealed AI\u2019s potential in remote learning, where virtual laboratories, telemedicine training, and AI-based proctoring ensured pedagogical continuity. Research consistently demonstrates that AI-enabled distance learning sustains or enhances retention and student satisfaction compared to traditional modalities. Despite its promise, challenges persist. Ethical issues of data privacy, financial costs, and the necessity of faculty training must be addressed to ensure equitable access. Additionally, cultural adaptation and institutional readiness influence adoption rates, particularly in resource-constrained regions.<\/p>\n

While the article effectively synthesizes current evidence and real-world applications, it may be strengthened by situating AI within broader educational theory (e.g., constructivist learning) and by comparing outcomes with traditional pedagogies at a meta-analytical level. Future research should focus on cost-effectiveness, faculty\u2013student co-design of AI tools, and long-term impact on patient care outcomes, ensuring that AI serves not only as a technological innovation but as a pedagogically sound transformation of medical education.<\/p>\n

Keywords: <\/strong>AI, Education, Benchmark, Georgia, Medicine<\/p>\n

JEL<\/strong>: I21; I23; O33<\/p>\n

DOI: <\/strong>10.52244\/c2025.17<\/p>\n

Article<\/strong><\/a><\/p>\n

References:<\/strong><\/p>\n

Berman, N., & Durning, S. (2021). Artificial intelligence in medical education: Trends and challenges. Medical Education, 55<\/em>(3), 456\u2013467. https:\/\/doi.org\/10.1111\/medu.14450<\/a><\/p>\n

Brown, L., Patel, V., & Roberts, A. (2021). Automated essay scoring in medical education. Academic Medicine, 96<\/em>(5), 682\u2013690. https:\/\/doi.org\/10.1097\/ACM.0000000000004012<\/a><\/p>\n

Cook, D. A., Hamstra, S. J., Brydges, R., Zendejas, B., Szostek, J. H., Wang, A. T., \u2026 Hatala, R. (2013). Comparative effectiveness of instructional design features in simulation-based education: Systematic review and meta-analysis. Medical Teacher, 35<\/em>(1), e867\u2013e898. https:\/\/doi.org\/10.3109\/0142159X.2012.714886<\/a><\/p>\n

Custers, E., & Ten Cate, O. (2020). Virtual patients and AI-driven learning in medical curricula. Advances in Health Sciences Education, 25<\/em>(4), 801\u2013815. https:\/\/doi.org\/10.1007\/s10459-019-09929-6<\/a><\/p>\n

Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects, 49<\/em>(1), 91\u201396. https:\/\/doi.org\/10.1007\/s11125-020-09464-3<\/a><\/p>\n

Ellaway, R., & Masters, K. (2008). AMEE Guide 32: E-Learning in medical education Part 1: Learning, teaching, and assessment. Medical Teacher, 30<\/em>(5), 455\u2013473. https:\/\/doi.org\/10.1080\/01421590802108331<\/a><\/p>\n

Johnson, R., White, P., & Patel, S. (2021). Adaptive AI question banks in healthcare learning. Medical Teacher, 43<\/em>(11), 1234\u20131245. https:\/\/doi.org\/10.1080\/0142159X.2021.1876589<\/a><\/p>\n

Lee, H., Kim, S., & Park, J. (2022). AI-assisted OSCE assessment: Outcomes and reliability. Medical Education Online, 27<\/em>(1), 201\u2013215. https:\/\/doi.org\/10.1080\/10872981.2021.201215<\/a><\/p>\n

Smith, J., Walker, T., & Davis, R. (2022). AI-guided remediation in medical education. Journal of Medical Education, 60<\/em>(2), 150\u2013165. https:\/\/doi.org\/10.1177\/10525629211065321<\/a><\/p>\n

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again.<\/em> Basic Books.<\/p>\n

Wartman, S. A., & Combs, C. D. (2018). Medical education must move from the information age to the age of artificial intelligence. Academic Medicine, 93<\/em>(8), 1107\u20131109. https:\/\/doi.org\/10.1097\/ACM.0000000000002044<\/a><\/p>\n

Sugand, K., Abrahams, P., & Khurana, A. (2015). The role of simulation in developing surgical skills. Journal of Surgical Education, 72<\/em>(2), 209\u2013216. https:\/\/doi.org\/10.1016\/j.jsurg.2014.08.003<\/p>\n

Lateef, F. (2010). Simulation-based learning: Just like the real thing. Journal of Emergencies, Trauma and Shock, 3<\/em>(4), 348\u2013352. https:\/\/doi.org\/10.4103\/0974-2700.70743<\/p>\n

Kneebone, R. (2016). Simulation in surgical training: Educational issues and practical implications. Medical Education, 50<\/em>(1), 104\u2013116. https:\/\/doi.org\/10.1111\/medu.12844<\/p>\n

Botezatu, M., Hult, H., & Fors, U. (2010). Virtual patients in medical education: An interactive learning model. Medical Teacher, 32<\/em>(12), 950\u2013957. https:\/\/doi.org\/10.3109\/0142159X.2010.506168<\/p>\n

Dede, C. (2011). Emerging technologies, ubiquitous learning, and educational transformation. Educational Technology, 51<\/em>(6), 3\u201314.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"gonisdziebebi":[103],"class_list":["post-35569","articles","type-articles","status-publish","hentry","gonisdziebebi-2025-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/cue.edu.ge\/en\/wp-json\/wp\/v2\/articles\/35569","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cue.edu.ge\/en\/wp-json\/wp\/v2\/articles"}],"about":[{"href":"https:\/\/cue.edu.ge\/en\/wp-json\/wp\/v2\/types\/articles"}],"wp:attachment":[{"href":"https:\/\/cue.edu.ge\/en\/wp-json\/wp\/v2\/media?parent=35569"}],"wp:term":[{"taxonomy":"gonisdziebebi","embeddable":true,"href":"https:\/\/cue.edu.ge\/en\/wp-json\/wp\/v2\/gonisdziebebi?post=35569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}