Alexander Hann

Professorship for Digital Transformation in Gastroenterology

University Hospital Würzburg

Germany

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Alexander Hann is a Professor for Digital Transformation in Gastroenterology at University Hospital Würzburg, Germany. His research areas focus on artificial intelligence in gastrointestinal applications, including real-time AI evaluation and immersive virtual reality training systems. Recent publications highlight advancements in colonoscopy practices, the impact of climate change on gastroenterology, and innovative approaches to gastroesophageal assessments.

Alexander Hann

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Articles (23)

Prospective Evaluation of Real‐Time Artificial Intelligence for the Hill Classification of the Gastroesophageal Junction

Validation of the ViGaTu Immersive Virtual Reality Endoscopy Training System for Physicians and Nurses.

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices.

Reducing scope 3 carbon emissions in gastrointestinal endoscopy: results of the prospective study of the 'Green Endoscopy Project Würzburg'.

Evaluation of a Virtual Reality-Based Open Educational Resource Software.

The United European Gastroenterology green paper-climate change and gastroenterology.

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning.

A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks.

Quality of life in benign colorectal disease—a review of the assessment with the Gastrointestinal Quality of Life Index (GIQLI)

The Role of Specialized Instruments for Advanced Endoscopic Resections in Gastrointestinal Disease.

Direct comparison of multiple computer-aided polyp detection systems.

Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.

Assessment of the yearly carbon emission of a gastrointestinal endoscopy unit.

Impact of pre-procedural testing on SARS-CoV-2 transmission to endoscopy staff.

A video based benchmark data set (ENDOTEST) to evaluate computer-aided polyp detection systems.

Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies.

Innovations in GI-endoscopy.

Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions.

Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists.

Pilot study of a new freely available computer-aided polyp detection system in clinical practice.

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy.

The influence of computer-aided polyp detection systems on reaction time for polyp detection and eye gaze.

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Karl-Hermann Fuchs

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