AUTHOR=Jha Debesh , Durak Gorkem , Das Abhijit , Sanjotra Jasmer , Susladkar Onkar , Sarkar Suramyaa , Rauniyar Ashish , Kumar Tomar Nikhil , Peng Linkai , Li Sirui , Biswas Koushik , Aktas Ertugrul , Keles Elif , Antalek Matthew , Zhang Zheyuan , Wang Bin , Zhu Xin , Pan Hongyi , Seyithanoglu Deniz , Medetalibeyoglu Alpay , Sharma Vanshali , Cicek Vedat , Rahsepar Amir A. , Hendrix Rutger , Cetin A. Enis , Aydogan Bulent , Abazeed Mohamed , Miller Frank H. , Keswani Rajesh N. , Savas Hatice , Jambawalikar Sachin , Ladner Daniela P. , Borhani Amir A. , Spampinato Concetto , Wallace Michael B. , Bagci Ulas 

TITLE=Ethical framework for responsible foundational models in medical imaging

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1544501

DOI=10.3389/fmed.2025.1544501

ISSN=2296-858X

ABSTRACT=The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning. These large-scale artificial intelligence systems, trained on extensive multimodal and multi-center datasets, demonstrate remarkable versatility across diverse medical applications. However, their integration into clinical practice presents complex ethical challenges that extend beyond technical performance metrics. This study examines the critical ethical considerations at the intersection of healthcare and artificial intelligence. Patient data privacy remains a fundamental concern, particularly given these models' requirement for extensive training data and their potential to inadvertently memorize sensitive information. Algorithmic bias poses a significant challenge in healthcare, as historical disparities in medical data collection may perpetuate or exacerbate existing healthcare inequities across demographic groups. The complexity of foundational models presents significant challenges regarding transparency and explainability in medical decision-making. We propose a comprehensive ethical framework that addresses these challenges while promoting responsible innovation. This framework emphasizes robust privacy safeguards, systematic bias detection and mitigation strategies, and mechanisms for maintaining meaningful human oversight. By establishing clear guidelines for development and deployment, we aim to harness the transformative potential of foundational models while preserving the fundamental principles of medical ethics and patient-centered care.