De-Identified and Still Exposed
A recent study highlights the privacy risks associated with foundation models trained on de-identified electronic health records. Researchers found that these models can memorize individual patient data, potentially exposing sensitive information. This issue raises significant concerns about patient privacy in the deployment of clinical artificial intelligence.
- ▪Foundation models trained on electronic health records may memorize individual patient data despite de-identification efforts.
- ▪The study presented at the 2025 NeurIPS conference introduced tests to evaluate whether models generalize medical knowledge or recall specific patient records.
- ▪Memorization can lead to privacy breaches, especially for patients with rare conditions or sensitive health information.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2478211) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Tim Green Posted on Mar 19 • Originally published at smarterarticles.co.uk on Jun 3 De-Identified and Still Exposed #humanintheloop #clinicalaimemorization #healthcareprivacyrisks #deidentificationfailures Somewhere inside a foundation model trained on millions of supposedly de-identified electronic health records, a ghost lingers.
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