Ethical considerations for teaching with artificial intelligence: a scoping review in medical education settings

In the evolving realm of medical education, where artificial intelligence (AI) technologies play an increasingly pivotal role, embedding AI ethics within educational frameworks has become crucial. This scoping review explores the breadth of literature concerning ethical considerations for teaching with AI in medical education settings. Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, the team analyzed 82 peer-reviewed English-language articles published between 2018 and October 2023, sourced from PubMed, Embase, and Web of Science. The review presents insights into the geographic distribution of studies, publication trends, article types, common keywords, areas of focus, and ethical considerations. Ethical considerations are framed around the core principles of autonomy, beneficence, non-maleficence, justice, and transparency. Emerging themes and notable gaps are identified, offering suggested directions for future research. Review articles were found to be the most common study type, and "artificial intelligence" and "education" were the most frequently recurring keywords in the articles. The articles primarily focused on (a) incorporating AI in medical education, (b) AI ethical principles in medical education, and (c) ChatGPT in healthcare education. Ethical concerns raised in the articles centered on privacy, bias, informed consent, and algorithmic transparency, with many offering implications for curriculum development. These findings underscore the significance of integrating AI tools as instructional aids to enhance medical education, improve student learning experiences, and address the ethical challenges associated with their use in instructional settings. Future research opportunities include refining standardized competencies for AI in medical education, developing evidence-based curricula, and conducting longitudinal studies to assess AI's long-term impact on learning outcomes.