“Doing no harm” in the digital age: navigating tradeoffs and operational considerations for privacy-preserving deep learning in medicine
As artificial intelligence (AI) is increasingly deployed in clinical care, protecting patient privacy has become a central challenge. Mohammadi et al. examine the emerging use of privacy-enhancing technologies in medical deep learning, and look at how formal privacy guarantees interact with model performance and fairness. Their findings reveal variability in performance-privacy tradeoffs across data types and tasks, and highlight the risk that privacy constraints may widen existing disparities if not carefully evaluated. Beyond performance, privacy and equity should also be considered as equal pillars of trustworthy medical AI.
Authors
- Ariel Yuhan Ong (ORCID: https://orcid.org/0000-0001-9300-573X)
- Joseph C. Kvedar (ORCID: https://orcid.org/0000-0002-7517-2291)
- Kyra L. Rosen
- Margaret Sui
Institutions
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- Harvard University (US)
- NIHR Moorfields Biomedical Research Centre (GB)
- Dana-Farber Cancer Institute (US)
- Moorfields Eye Hospital (GB)
- University College London (GB)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-03-11
- DOI
- https://doi.org/10.1038/s41746-026-02549-x
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Yuhan
- National Institute for Health and Care Research
- University College London
- Moorfields Eye Hospital NHS Foundation Trust
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology