112 citations · 215 across the 17 of their papers we have counts for
18 papers
FRAME: separating sampling variation from representational cause in medical imaging fairness
Mahshad Lotfinia, Daniel Truhn, Andreas Maier +1
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. He…
Self-supervision drives representational convergence in medical foundation models more than clinical supervision
Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia +4
Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations on…
Vision-language models for chest radiography do not always need the image
Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams +3
Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image. That inference is unsafe: a model exploit…
Cross-modal linkage risk in clinical vision-language models
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung +1
Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.…
Safety and accuracy follow different scaling laws in clinical large language models
Sebastian Wind, Tri-Thien Nguyen, Jeta Sopa +9
Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation that higher accuracy implies…
Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision
Soroosh Tayebi Arasteh, Mehdi Joodaki, Mahshad Lotfinia +2
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the tar…