Previously submitted to: JMIR Medical Education (no longer under consideration since Jun 04, 2025)
Date Submitted: Feb 12, 2025
Open Peer Review Period: Feb 13, 2025 - Apr 10, 2025
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Artificial Intelligence in Medical Education: A Narrative Review of Challenges, Ethical Implications, and Practical Solutions
ABSTRACT
Background:
Background:
Artificial Intelligence is rapidly transforming medical education by offering personalized learning, diagnostic training, and adaptive teaching methods. However, its integration poses several challenges, including ethical concerns, biases, data privacy issues, and pedagogical implications. A thorough understanding of these challenges is crucial for the responsible implementation of AI-driven education in healthcare. Addressing these challenges requires a balanced approach that includes ethical oversight, transparent AI governance, faculty training, and curriculum adaptation to ensure AI enhances rather than replaces traditional educational methods.
Objective:
Objective:
This narrative review explores the impact of AI on medical education, with a particular focus on ethical challenges. It aims to synthesize current literature to identify ethical challenges & solution strategies. The goal is to promote responsible AI integration that ensures fairness, transparency, and effectiveness in medical training while preserving ethical integrity.
Methods:
Methods:
A selective literature search was conducted using databases such as PubMed, Scopus, and Google Scholar to identify peer-reviewed studies, conference proceedings, and reports published between 2015 and 2025. Key themes, including technological barriers, ethical concerns, and pedagogical implications, were analyzed using thematic synthesis. As a narrative review, no formal quality assessment or meta-analysis was performed.
Results:
Results:
AI-driven medical education faces ethical and implementation challenges. Biases in AI algorithms can lead to disparities in student assessments, while data privacy risks necessitate stringent governance frameworks. Faculty resistance, limited infrastructure, and insufficient digital literacy further hinder AI adoption. Additionally, concerns arise regarding AI's impact on critical thinking, clinical reasoning, and humanistic aspects of education. Addressing these issues requires ethical AI design, faculty training, and policy updates to ensure fairness, transparency, and accountability.
Conclusions:
Conclusion: AI has the potential to revolutionize medical education, but its integration must be approached cautiously to mitigate ethical, technological, and pedagogical challenges. Institutions must adopt adaptive policies, ensure diversity in AI datasets, and emphasize human oversight in AI-driven learning environments. Future research should focus on developing standardized AI literacy programs for educators and students, alongside robust ethical frameworks for AI governance in medical education.
Citation
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