Ethical issues in medical AI education: challenges and prospects

The rapid integration of artificial intelligence (AI) into clinical practice presents a paradigm shift in healthcare, necessitating a parallel evolution in medical education. While current curricula increasingly address the technical functionalities of AI, a critical gap remains in the systematic education of medical students and professionals on the profound ethical implications of these technologies. Given this, we screened out research articles published in recent years on the ethical issues of AI in medical education, resulting in a total of 26 papers. We conducted a scoping review of these 26 articles along with other relevant literature. This review argues that the failure to embed robust, critical, and nuanced AI ethics education within medical training is not merely an academic oversight but an impending catalyst for ethical crises in clinical care. It delineates the core ethical challenges—including algorithmic bias, opacity (“black box” problem), accountability, data privacy, and the erosion of professional autonomy—and critiques the prevailing, often superficial, approaches to teaching them. The review further proposes a transformative educational framework that moves beyond simplistic ethics modules towards a pervasive, interdisciplinary, and case-based integration of AI ethics across the entire medical curriculum. This approach aims to cultivate a generation of “digitally literate physicians” who are not passive consumers of AI outputs but critical, empowered, and ethically discerning partners in the age of intelligent medicine.