Ethical Framework for Responsible Foundational Models in Medical Imaging
It addresses ethical concerns for clinicians and patients in healthcare, but is incremental as it builds on existing ethical discussions without introducing new methods.
The paper tackles the ethical challenges of deploying foundational models in medical imaging by proposing a framework to guide their responsible development, focusing on issues like privacy, bias, and transparency to enhance patient welfare and trust.
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.