Ethical framework for responsible foundational models in medical imaging
Open accessAbstract
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.
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Lack of transparency in AI's decision-making processes undermines the trust of healthcare professionals and patients.
“Furthermore, the lack of transparency in AI’s decision-making processes undermines the trust of healthcare professionals and patients, posing an ethical and regulatory challenge essential for the sustainable development of the technology [60,63].”
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- Authors
- Jha, D., Durak, G., Das, A., Sanjotra, J., Susladkar, O., Sarkar, S., Rauniyar, A., Kumar Tomar, N., Peng, L., Li, S., Biswas, K., Aktas, E., Keles, E., Antalek, M., Zhang, Z., Wang, B., Zhu, X., Pan, H., Seyithanoglu, D., Medetalibeyoglu, A., Sharma, V., Cicek, V., Rahsepar, A. A., Hendrix, R., Cetin, A. E., Aydogan, B., Abazeed, M., Miller, F. H., Keswani, R. N., Savas, H., Jambawalikar, S., Ladner, D. P., Borhani, A. A., Spampinato, C., Wallace, M. B., & Bagci, U.
- Affiliations
- Northwestern University (US)
- SINTEF Digital (NO)
- University of Chicago (US)
- Columbia University (US)
- ORCID
- Jha, Debesh
- Das, Abhijit
- Susladkar, Onkar
- Rauniyar, Ashish
- Peng, Linkai
- Li, Sirui
- Biswas, Koushik
- Antalek, Matthew
- Zhang, Zheyuan
- Wang, Bin
- Sharma, Vanshali
- Rahsepar, Amir A.
- Hendrix, Rutger
- 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.
- Venue
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Published
- 2025-05-19
- Volume
- 12
- Pages
- 1544501-1544501
- ISSN
- 2296-858X (ISSN-L)
- PMC
- PMC12128638
- OpenAlex
- W4410495919
- Cited by
- 21
- References
- 58
- Citations by year
- 2026: 13 · 2025: 8
- FWCI
- 1.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- National Institutes of Health (R01-CA240639, R01-CA246704, R01-HL171376, U01-CA268808)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- foundation models, large language models, diagnostic imaging, AI ethics, patient data privacy, algorithmic bias, health care inequities, explainability, transparency, human oversight, responsible AI deployment, medical ethics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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