Medical Education Reform in the Age of Artificial Intelligence: Digital Transformation and Innovative Teaching Models

DOI

10.2991/978-2-38476-601-7_19How to use a DOI?

Keywords

Artificial Intelligence; Medical Education; Digital Transformation; Personalized Learning; Innovative Teaching Models

Abstract

The integration of Artificial Intelligence (AI) into medical education marks a paradigm shift, transitioning traditional pedagogical approaches toward highly personalized and data-driven methods. This paper explores the digital transformation of medical training between 2021 and 2026, analyzing the deployment of large language models (LLMs), natural language processing (NLP), and machine learning in curriculum design and clinical practice. We propose an AI-assisted educational framework that combines personalized adaptive learning with mixed-reality teaching models. Case studies showing the use of AI in medical imaging analysis and NLP-driven virtual standardized patients are examined to show effectiveness. Furthermore, we evaluate the ethical dimensions, addressing algorithm-based bias, data privacy, and the need to preserve humanistic care in patient-physician interactions. The findings suggest that while AI acts as a powerful assistant, strategic institutional readiness and strong governance are required to realizing its full potential in shaping the next generation of medical professionals.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Juan Zhang
AU  - Da Teng
PY  - 2026
DA  - 2026/08/06
TI  - Medical Education Reform in the Age of Artificial Intelligence: Digital Transformation and Innovative Teaching Models
BT  - Proceedings of the 2026 2nd International Conference on Education Reform, Ideology and Politics (ERIP 2026)
PB  - Atlantis Press
SP  - 145
EP  - 152
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-601-7_19
DO  - 10.2991/978-2-38476-601-7_19
ID  - Zhang2026
ER  -