MSc Thesis: Automatic recognition of billing codes based on dental documentation using Artificial Intelligence

We are an international team of experts in the field of NLP and AI looking for a highly motivated Master’s student to join us for a master thesis project. Our main focus is the development of Doctos, an app that generates dental documentation through speech-to-text and converts the documentation into relevant billing codes using Named Entity Recognition. The documentation and the generated billing codes are automatically imported into the relevant PVS. The objective of this thesis is to compare different methods for automatic recognition of billing codes from dental documentation. In particular, the focus will be on Named Entity Recognition techniques and their implementation in Doctos.


  • Computer Science, Mathematics or similar background
  • Strong background in NLP and machine learning
  • Interest in medical documentation and healthcare technology
  • Proficient in Python
  • Experience with Huggingface and NLP frameworks such as PyTorch, Tensorflow, or Keras (optional)
  • Fluent in German

What we offer:

  • A close, personal supervision
  • A unique opportunity to work in an interdisciplinary team and contribute to the development of Doctos
  • Access to cutting-edge technologies and techniques in NLP and AI
  • A unique opportunity to contribute to the development of a game-changing app in the healthcare industry.

If you are interested in this opportunity, please get in touch with us to discuss further details and to find an individual topic! This project is in collaboration with Doctos and under supervision of Bory Chibisov ( and Prof. Pförringer.

Daniel Rückert
Daniel Rückert
Professor of Artificial Intelligence in Healthcare and Medicine