Teaching AI ethics in medical schools: a scoping review protocol on the ethical–technical balance in curricular frameworks

Journal article

Introduction: The rapid integration of artificial intelligence (AI) technologies in healthcare, ranging from diagnostic tools to clinical decision support systems, is transforming medical practice and education. However, without deliberate integration of ethics, there is a risk that medical education will reproduce a technosolutionist orientation by privileging efficiency and data-driven outputs over patient autonomy, justice and professional integrity. While AI-related courses are increasingly being introduced into medical curricula, ethical considerations often remain peripheral, with most frameworks emphasising technical skills over moral reasoning. As future clinicians will face complex ethical challenges related to autonomy, safety, bias, transparency and accountability in AI-integrated clinical settings, there is an urgent need to evaluate how ethics is incorporated into AI education. With AI curricula still in their formative stages, this moment presents a critical opportunity to proactively design ethical components, rather than introducing them after harms have emerged. This scoping review aims to systematically map the ethical–technical balance in AI-related medical education curricula, identifying current practices, gaps and opportunities for curriculum development. Methods and analysis: This scoping review will follow the Joanna Briggs Institute methodology and be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. The review will address how ethical considerations are integrated into AI-related curricula in medical education and examine the balance between ethical and technical content. A comprehensive search strategy will be employed across multiple databases, including MEDLINE, Web of Science, Google Scholar, EBSCO, the Virtual Health Library, the Bioethics Literature Database and PhilPapers, as well as grey literature sources such as institutional reports, curricula and policy documents. Publications from January 2020 to December 2025 will be included. Data will be charted and analysed using descriptive qualitative content analysis, followed by a theory-informed interpretive analysis drawing on the hidden curriculum theory of medical education. Ethics and dissemination: This review does not require ethics approval, as it involves analysis of publicly available data. Findings will be disseminated through a peer-reviewed publication and presented at relevant conferences and workshops focused on medical education or bioethics.

Authors: Bardakçı, T; Barış, M; Dabbagh, H; Savulescu, J; Saraçoğlu, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BMJ Publishing Group
• Journal: BMJ Open
• Volume: 16
• Issue: 6
• Pages: e111721
• Article number: bmjopen-2025-111721
• Publication date: 2026-06-30
• Acceptance date: 2026-06-16
• DOI: 10.1136/bmjopen-2025-111721
• EISSN: 2044-6055
• ISSN: 2044-6055
• Language: English
• Keywords: MEDICAL EDUCATION & TRAINING; MEDICAL ETHICS; Artificial Intelligence