Department of Radiology (including Pediatric Radiology) 

Meta menu:

From here, you can access the Emergencies page, Contact Us page, Accessibility Settings, Language Selection, and Search page.

  • Go to Emergencies.
  • Current language selection: EN

    Change language selection to:

  • Contact us:

    Campus Charité Mitte: Charitéplatz 1Local address: Luisenstr. 10 (1st floor, room 024)
    10117 Berlin

    Campus Virchow-Klinikum: Augustenburger Platz 1 Local address: Südstraße 3 (groundfloor, room 0.0056)
    13353 Berlin

    Campus Benjamin Franklin: Hindenburgdamm 30Haus V, elevator 13/14 (1st floor)
    12203 Berlin

  • Accessibility:
    Contrast Settings Change contrast
    Font size
    Font size bigger: STRG+ Font size smaller: STRG-

    You can enlarge or reduce the browser window. Please use CTRL and + to zoom in or CTRL and - to zoom out. Press CTRL and 0 to reset your browser window to normal size.

Open Menu
Foto radiologisches Bild

Artificial Intelligence Lab

Interdisciplinary team developing AI solutions in radiology and implementing them in routine clinical practice.

You are here:

Page contents

Overview

The Artificial Intelligence (AI) lab of the Charité Department of Radiology consists of an interdisciplinary team with many years of experience in diagnostic and interventional radiology as well as in the development and application of AI applications for use in clinical and research settings.

The aim of the working group is to implement AI tools in everyday clinical practice for diagnostic support and to optimize care structures.

The team works closely with clinical working groups from other disciplines as well as PACS companies and software developers.

Research Focus:

  • Deep Learning for Computer Vision with focus on multidimensional radiological image data.
  • Natural Language Processing of radiological report texts
  • Database development and process optimization
  • PACS integration of Deep Learning models and workflow optimization 

 

Team

Prof. Dr. med. Dr. rer. medic. Stefan Markus Niehues

Deputy clinic director CBF, chief medical officer

  • Contact us
Porträtfoto
Dr. med. Janis Lucas Vahldiek

Senior Physician

  • Contact us
PD Dr. med. Keno Bressem

Specialist

  • Contact us
Porträtfoto
Dr. med. Nils Grauhan

Resident doctor

  • Contact us
Markus Hinsche

Data scientist

  • Contact us
Hans-Martin Thieß

Resident doctor

  • Contact us

PhD students

Bejschad Bashian

PhD student

  • Contact us
Zweifarbiger Schattenriss eines Männerkopfes; grau auf hellgrau.
Luise Franz

PhD student

  • Contact us
Kristin Grumm

PhD Student

  • Contact us
Lukas Kähler

PhD student

  • Contact us
Zweifarbiger Schattenriss eines Männerkopfes; grau auf hellgrau.
Gyeongphill Kang

PhD student

  • Contact us
Julia Kremmin

PhD student

  • Contact us
Zweifarbiger Schattenriss eines Frauenkopfes; grau auf hellgrau.
Jan Philipp Loyen

PhD student

  • Contact us
Zweifarbiger Schattenriss eines Männerkopfes; grau auf hellgrau.
Maximilian Rattunde

PhD student

  • Contact us
Maximilian Winter

PhD student

  • Contact us
Lina Xu

PhD student

  • Contact us

Publications on PubMed and Researchgate

Publications Pubmed/Medline-Collection
Prof. Niehues on ResearchGate Other researchers and groups
Dr. med. Lisa Adams on Researchgate Other researchers and groups
Dr. med. Keno Bressem on Researchgate Other researchers and groups

Important Publications

  • Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance

    Article: Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance

  • Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine

    Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine

  • Deep learning for accurately recognizing common causes of shoulder pain on radiographs

    Article: Deep learning for accurately recognizing common causes of shoulder pain on radiographs

  • Improving CT accuracy in the diagnosis of COVID-19 in a hospital setting

  • Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports

    Article: Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports

  • Comparing different deep learning architectures for classification of chest radiographs

    Article: Comparing different deep learning architectures for classification of chest radiographs