Matters Arising: Near-identical images, not foundation models, explain purported re-identification of patients from medical imaging

Nebbia et al. <sup>1</sup> report that foundation models enable re-identification of patients in medical imaging datasets, given a known medical image of the same type of targeted individual. They postulate that this is due to the ability of foundation models to identify attributes such as age, sex or ethnicity of a person from said imaging and identify this as a risk stemming from medical foundation model research.

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Publication Details

Journal
npj Digital Medicine
Published
2026-02-13
DOI
https://doi.org/10.1038/s41746-026-02440-9
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Matters Arising: Near-identical images, not foundation models, explain purported re-identification of patients from medical imaging

Pearse A. Keane, Siegfried K. Wagner, Justin Engelmann, Yukun Zhou
npj Digital Medicine
Face recognition and analysis
article

Matters Arising: Near-identical images, not foundation models, explain purported re-identification of patients from medical imaging

Pearse A. Keane, Siegfried K. Wagner, Justin Engelmann, Yukun Zhou
article en

Abstract

Nebbia et al. <sup>1</sup> report that foundation models enable re-identification of patients in medical imaging datasets, given a known medical image of the same type of targeted individual. They postulate that this is due to the ability of foundation models to identify attributes such as age, sex or ethnicity of a person from said imaging and identify this as a risk stemming from medical foundation model research.

npj Digital MedicineVol. 9(1)
Moorfields Eye Hospital NHS Foundation Trust (GB), Institute of Ophthalmology (MX), Moorfields Eye Hospital (GB), University College London (GB)
Gender equality
Openalex Percentile: Top 57%
Face recognition and analysis
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