Author

Christoph Matthias

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Open access Aug 2026

Workflow evaluation of a commercial deep learning-based image analysis tool for the quantification of angiogenesis in vivo

Angiogenesis plays a crucial role in tumor development as well as in wound healing and integration of biomaterials. However, quantification of complex vascular networks across different in vivo models is time-consuming, labor-intensive, and prone to human error. Deep learning-based analysis (DLBA) appears to be a more...

C. Buhr, A. Maas, Nadine Wiesmann-Imilowski et al. · Scientific Reports · 0 citations

Open access Aug 2026

Augmenting Head and Neck Multidisciplinary Tumor Board Recommendations With Locally Run Large Language Models: Prospective Evaluation of Real-World Implementation

The data demonstrate that the integration of LLMs in today’s MDT workflow is feasible and may benefit the quality of decision-making in specific cases, and suggests that more advanced local models may offer safe, rapid, and cost-effective support for MDT decision-making.

C. Buhr, L. Müller, D. Pinto dos Santos et al. · JMIR AI · 0 citations

Review Open access Jul 2026

Patients' perception towards large language models in otorhinolaryngology, head and neck surgery: a single-centre survey

OrL-HNS patients are largely familiar with LLMs and frequently use them, but their trust and confidence regarding health information provided by LLMs alone is limited, suggesting that clinician-led LLM use may be acceptable to many patients.

C. Buhr, A. Blaikie, Harry Smith et al. · Frontiers in Digital Health · 0 citations