Artificial intelligence has emerged as a transformative force across healthcare systems, with medical education representing one of its most consequential domains of influence. The rapid integration of artificial intelligence–based tools into medical curricula has altered how knowledge is delivered, acquired, assessed, and retained, while simultaneously reshaping the roles of educators and learners. This article presents an extensive, theory-driven examination of artificial intelligence in medical education, grounded strictly in established scholarly literature. Drawing upon contemporary research, the study explores current applications such as adaptive learning systems, machine learning–assisted assessment, intelligent tutoring, virtual patients, and simulation-based environments. Particular emphasis is placed on how these technologies address long-standing pedagogical challenges, including variability in learner preparedness, limitations of traditional assessment models, and constraints imposed by clinical training environments. The article also situates artificial intelligence within the broader disruption caused by the COVID-19 pandemic, examining how global educational crises accelerated digital and intelligent innovation. Ethical considerations are explored in depth, including algorithmic bias, data governance, transparency, accountability, and the evolving professional identity of future physicians. Through a descriptive and interpretive methodological approach, this work synthesizes existing evidence to identify both opportunities and risks associated with artificial intelligence–driven education. The findings suggest that while artificial intelligence holds significant promise for personalizing learning and improving educational outcomes, its successful and responsible implementation depends on robust ethical frameworks, faculty development, and institutional governance. The discussion highlights theoretical implications for medical pedagogy, outlines limitations in current research, and proposes future directions for scholarly inquiry and policy development. Ultimately, the article argues that artificial intelligence should be viewed not as a replacement for human educators, but as a powerful augmentation capable of reimagining medical education in alignment with societal needs and professional values.

Artificial intelligence, medical education, adaptive learning

hen, J., Zhang, Y., & Li, X. (2022). Artificial intelligence in medical education: Current applications and future perspectives. Medical Education Online, 27(1), 2022643.

Dedeilia, A., et al. (2020). Medical and surgical education challenges and innovations in the COVID-19 era. In Vivo, 34(3), 1603–1611.

Khan, R., et al. (2021). Adaptive learning technologies in medical education. Advances in Health Sciences Education, 26(2), 553–567.

Kolachalama, V. B., & Garg, P. (2018). Machine learning and medical education. npj Digital Medicine, 1, 54.

Masterson, J., O’Connor, M., & McNamara, D. (2020). Virtual patients and AI simulation in medical training. BMC Medical Education, 20(1), 342.

Wartman, S. A., & Combs, C. D. (2019). Reimagining medical education in the age of AI. npj Digital Medicine, 2, 69.

Zhang, Y., & Lu, X. (2021). Ethical considerations of AI in medical education. Medical Teacher, 43(7), 789–796.