works on

From the 1 of 18 linked papers with an AI index.

most citedAddressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

2 citations · 2 across the 2 of their papers we have counts for

collaborators

18 papers

cs.CV2026

International Transfer of Stochastic Cortical Self-Reconstruction

Fabian Bongratz, Zhizheng Zhuo, Chao Zhang +3

Stochastic cortical self-reconstruction (SCSR) enables personalized mapping of gray matter atrophy, a hallmark of neurodegenerative disorders such as Alzheimer's disease (AD), onto…

cs.LG20262 cited

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Jiazhen Pan, Bailiang Jian, Paul Hager +19

The paper presents a dynamic red‑teaming framework (DAS) that continuously stress‑tests large language models on health tasks for robustness, privacy, bias, and hallucination, reve…

cs.LG2026

Routine laboratory trajectories encode the onset of organ-level complications in cancer

Jannik Lübberstedt, Krischan Braitsch, Jacqueline Lammert +21

Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-time…

cs.CV2026

DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation

Emre Kavak, Tom Nuno Wolf, Christian Wachinger

Dataset bias often leads deep learning models to exploit spurious correlations instead of task-relevant signals. We introduce the Standard Anti-Causal Model (SAM), a unifying causa…

cs.CV2026

Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging

Yitong Li, Morteza Ghahremani, Christian Wachinger

Vision-language foundation models achieve promising performance in natural image classification, yet their direct application to medical imaging is limited by severe domain shifts,…

cs.CV2026

Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering

Christian Wachinger, Bernhard Renger, Christopher Späth +2

Interpreting quantitative CT biomarkers, such as organ volume and tissue attenuation, requires large-scale healthy reference distributions. However, creating these is challenging b…