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Ethical Framework for Responsible Foundational Models in Medical Imaging

Abhijit Das, Debesh Jha, Jasmer Sanjotra, Onkar Susladkar, Suramyaa Sarkar, Ashish Rauniyar, Nikhil Tomar, Vanshali Sharma, Ulas Bagci

TL;DR

Foundational models hold promise for medical imaging but raise critical ethical issues around privacy, bias, transparency, and accountability in clinical deployment. The authors propose an end-to-end ethical framework built on glass-box interpretability, federated learning, LLM-enabled regulatory support, and privacy-preserving generative AI, complemented by fairness and privacy metrics and governance. Key contributions include actionable guidelines for data provenance, auditing, regulatory alignment, and stakeholder engagement, along with discussions of copyright, scaling, and safety. The work aims to enable trustworthy, patient-centered adoption of foundational models in medical imaging, reducing risk while unlocking clinical benefits.

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.

Ethical Framework for Responsible Foundational Models in Medical Imaging

TL;DR

Foundational models hold promise for medical imaging but raise critical ethical issues around privacy, bias, transparency, and accountability in clinical deployment. The authors propose an end-to-end ethical framework built on glass-box interpretability, federated learning, LLM-enabled regulatory support, and privacy-preserving generative AI, complemented by fairness and privacy metrics and governance. Key contributions include actionable guidelines for data provenance, auditing, regulatory alignment, and stakeholder engagement, along with discussions of copyright, scaling, and safety. The work aims to enable trustworthy, patient-centered adoption of foundational models in medical imaging, reducing risk while unlocking clinical benefits.

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.
Paper Structure (13 sections, 1 figure)

This paper contains 13 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Challenges of foundation models: Overview
  3. Method
  4. Glass box FMs: Towards transparency
  5. Federated learning: Ensuring privacy
  6. LLMs: Facilitating regulatory compliance
  7. Generative AI: Generalization with privacy
  8. Fairness, biases, and risks with generative foundational models
  9. Methods for measuring fairness, bias, privacy, and diversity of generations
  10. Copyright concerns
  11. Balance between scaling and societal impact for foundational models
  12. Towards ethical and responsible use
  13. Conclusion

Figures (1)

  • Figure 1: Comprehensive workflow of a foundational model in medical imaging: From multi-modal data acquisition and curation to deployment and iterative model update, incorporating ethical considerations and downstream tasks for higher order skills and vision tasks.