Re-identification of patients from imaging features extracted by foundation models

作者：Giacomo Nebbia, Sourav Kumar, Stephen Michael McNamara, Christopher P. Bridge, J. Peter Campbell, Michael F. Chiang, Naresh Mandava, Praveer Singh, Jayashree Kalpathy-Cramer · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-01801-0 · 被引用次数：8 · 研究领域：COVID-19 diagnosis using AI、AI in cancer detection、Medical Imaging and Analysis

Foundation models for medical imaging are a prominent research topic, but risks associated with the imaging features they can capture have not been explored. We aimed to assess whether imaging features from foundation models enable patient re-identification and to relate re-identification to demographic features prediction. Our data included Colour Fundus Photos (CFP), Optical Coherence Tomography (OCT) b-scans, and chest x-rays and we reported re-identification rates of 40.3%, 46.3%, and 25.9%, respectively. We reported varying performance on demographic features prediction depending on re-identification status (e.g., AUC-ROC for gender from CFP is 82.1% for re-identified images vs. 76.8% for non-re-identified ones). When training a deep learning model on the re-identification task, we reported performance of 82.3%, 93.9%, and 63.7% at image level on our internal CFP, OCT, and chest x-ray data. We showed that imaging features extracted from foundation models in ophthalmology and radiology include information that can lead to patient re-identification.