Author

J. Kather

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Preprint Aug 2026

VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology

Pathology vision-language models are advancing rapidly, yet existing benchmarks remain focused on human tissue, particularly oncology, leaving non-human pathology largely unaddressed. This gap is especially important in toxicologic pathology, where microscopic tissue examination of laboratory animals is a core componen...

Luca L. Weishaupt, S. de Brot, Javier Asin et al. · 0 citations

#computer vision Jul 2026

Self-supervision drives representational convergence in medical foundation models more than clinical supervision

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

Jul 2026

Bayesian uncertainty estimation improves clinical decision making in medical AI agents

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

Jul 2026

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans

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

Preprint Aug 2026

Large language models improve physician accuracy but lead to false reliance

These findings show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk, and show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk.

T. Chanda, C. Wies, Franziska Schramm et al. · 0 citations

Open access Feb 2026

Large Language Models for Oncology Guideline Maintenance: Prospective Case Study

Abstract Background Maintenance of oncology clinical practice guidelines (CPGs) is increasingly challenged by the rapid growth of trial data and therapeutic complexity. While large language models (LLMs) have shown promise in information retrieval, their utility in the rigorous, end-to-end workflow of guideline mainten...

Manuel Knauer, Julian Greß, J. Kather et al. · JMIR AI · 0 citations