Fair-model Reference And Mechanism Evaluation (FRAME) compares each reported difference with the difference expected from a perfectly fair model at the same subgroup counts and tests whether candidate causes of any excess over this fair-model reference change the difference when they are injected into model features.
Mahshad Lotfinia, D. Truhn, Andreas K. Maier et al.· 0 citations
Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read disease-specific findings or shortcuts that correlate with the diagnosis. We tested this in 221 electronic-health-record (EHR) phenotypes using Auditable CT phenotyping (AC...
Riga Wu, W. Witschey, Yi-Cheng Li et al.· 0 citations
Differential privacy (DP)'s effect in medical imaging is typically evaluated only through end-to-end performance, leaving the mechanism of privacy-induced utility loss unclear. We introduce Differential Privacy Representation Geometry for Medical Imaging (DP-RGMI), a framework that interprets DP as a structured transfo...
S. T. Arasteh, Marziyeh Mohammadi, S. Nebelung et al.· arXiv.org· 0 citations
Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similar...
Soroosh Tayebi Arasteh, S. Ziegelmayer, Mahshad Lotfinia et al.· arXiv.org· 0 citations
Subgroup performance differences are the standard evidence for fairness bias in medical imaging, and the usual response removes the demographic information that a model encodes. Here we introduce Fair-model Reference And Mechanism Evaluation (FRAME), a two-step framework for auditing such a claim. The first step derive...
Mahshad Lotfinia, D. Truhn, Andreas K. Maier et al.· 0 citations
It is shown that Monte Carlo dropout, applied to a multi-task chest-radiograph classifier, provides an epistemic uncertainty signal that tracks generalisation across training-set scales and flags confident yet error-prone predictions.
Frederik Hauke, P. Wienholt, Christiane Kuhl et al.· arXiv.org· 0 citations
It is demonstrated that pairing a genomic language model with widely available clinical imaging can serve as a hypothesis-free discovery tool for gene--imaging associations invisible to conventional approaches.
Frederik Hauke, Jeremias Krause, P. Wienholt et al.· arXiv.org· 0 citations
The Review examines rapid LLM adoption in clinical care, outlining emerging security and safety risks across development stages, key protective layers, clinically relevant threats and current mitigation responsibilities in a single integrated framework.
J. Clusmann, O. Freyer, Max Ostermann et al.· Nature· 1 citation
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