22 citations · 36 across the 5 of their papers we have counts for
5 papers
FRAME: Separating sampling variation from performance disparities in medical image analysis
Mahshad Lotfinia, Daniel Truhn, Andreas Maier +1
Fairness audits of medical imaging models commonly report the largest performance difference between demographic subgroups and treat any positive value as evidence of bias. Yet a p…
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…
SteuerLLM: Local specialized large language model for German tax law analysis
Sebastian Wind, Jeta Sopa, Laurin Schmid +8
Large language models (LLMs) demonstrate strong general reasoning and language understanding, yet their performance degrades in domains governed by strict formal rules, precise ter…
Multi-step retrieval and reasoning improves radiology question answering with large language models
Sebastian Wind, Jeta Sopa, Daniel Truhn +9
Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-aug…
Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
Marziyeh Mohammadi, Mohsen Vejdanihemmat, Mahshad Lotfinia +4
Differential privacy (DP) is a key technique for protecting sensitive patient data in medical deep learning (DL). As clinical models grow more data-dependent, balancing privacy wit…