  Foundational models (FMs) have tremendous potential to revolutionize medical
imaging. However, their deployment in real-world clinical settings demands
extensive ethical considerations. This paper aims to highlight the ethical
concerns related to FMs and propose a framework to guide their responsible
development and implementation within medicine. We meticulously examine ethical
issues such as privacy of patient data, bias mitigation, algorithmic
transparency, explainability and accountability. The proposed framework is
designed to prioritize patient welfare, mitigate potential risks, and foster
trust in AI-assisted healthcare.
