Ethical Framework for Responsible Foundational Models in Medical Imaging

TL;DR

This paper discusses ethical challenges in deploying foundational models in medical imaging and proposes a framework to ensure responsible development, emphasizing patient privacy, bias mitigation, transparency, and accountability.

Contribution

It introduces a comprehensive ethical framework tailored for the responsible use of foundational models in medical imaging, addressing key ethical concerns.

Findings

Framework emphasizes patient welfare and trust

Addresses privacy, bias, transparency, and accountability

Guides ethical deployment in clinical settings

Abstract

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.