Ethical Perceptions and Practices of Artificial Intelligence in Medical Education: A Systematic Review.
Abstract
Objective: Artificial intelligence (AI) is increasingly used in medical education, offering opportunities for learning, research, and academic productivity while raising concerns about academic integrity and responsible use. This systematic review aimed to synthesize empirical evidence on ethical perceptions and practices related to AI use among medical students and educators. Methodology: Following PRISMA 2020 guidelines, PubMed and Scopus were searched for English-language studies published between January 2020 and March 2026. Original studies using structured, closed-ended assessments of ethical issues related to AI use in medical education were included. Two reviewers independently screened studies and extracted data. Methodological quality was assessed using the Appraisal Tool for Cross-Sectional Studies (AXIS), and findings were synthesized narratively. Results: Eight cross-sectional studies met the inclusion criteria, including six student-focused and two educator-focused studies. Although AI was generally viewed positively, recurring concerns included plagiarism, academic misconduct, misinformation, algorithmic bias, privacy risks, and overreliance on AI. Student-focused studies more frequently highlighted practical educational benefits, whereas educator-focused studies placed greater emphasis on assessment integrity, educational quality, and institutional guidance. However, differences in study populations and assessment methods limited direct comparisons. Conclusions: The findings highlight the need for clearer institutional guidance, AI literacy education, and responsible curriculum integration. However, the available evidence is predominantly based on self-reported perceptions and does not establish the effectiveness of specific educational interventions. Further research is needed to examine actual AI-related practices and their ethical and educational implications.
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