Digitalization in medicine - artificial intelligence in the diagnosis of rare diseases

Research Group Leader

  • Prof. Dr. Annette D. Wagner


Research Group Members

  • Dr. rer. nat. Dipl. Psychologin Anne Pankow
  • cand. hum. biol. Lukas Völkel
  • cand. med. dent. Lukas Willmen


Scientific profile

The research focuses on investigating the possibilities of digitalisation in medicine - in particular the possible use of artificial intelligence in the diagnosis of rare diseases. In a project with Ada Health, we evaluated a digital diagnosis assistance system for rare diseases.The aim of the system is to support physicians in the correct and timely identification of complex and rare diseases in order to enable targeted referral of patients to rare disease centres and promptly initiate adequate therapy. Improved quality of referrals as an effect of this system leads to cost reductions in the health care system. Building on these results a continuation of the project is planned involving further internal and external research institutions.

On the basis of the results of the retrospective study previously published, various subgroups of rare diseases will be prospectively investigated in the future. The study  also aims to investigate descriptively the accuracy of a self-assessment system in an application for specific groups of rare diseases. Another focus is the optimisation of self-assessment technology for the identification of a wide range of rare diseases. If successfully implemented, significant effects on the possibility of identifying affected patients and thus on their time to diagnosis are to be hoped for. A critical primary endpoint of this prospective study is the concordance of the disease suggested by the app compared to the confirmed diagnosis of the patient(s).

As part of the development of new teaching concepts, the teaching of digital competence is planned. The teaching formats aim at providing the students with fundamental and sustainable digital skills beyond the specific field, putting lifelong learning and doctor-patient dialogue on a new footing. In this way, the project is intended to provide impulses for medical studies and the doctor-patient relationship of the future.The students will use diagnostic support systems themselves both in an interactive seminar and at the bedside. The possibilities and limitations of the systems will be critically examined in various scenarios. The basic relevant aspects of artificial intelligence will be explained in order to convey the potentials of the systems to them.

Accurate clinical phenotyping has enabled the clustering of different patient groups with rare systemic diseases. These subgroups are mostly diseases whose genetic basis is not yet known. For this reason, future patient groups with a significant number of cases will be examineded for common genetic alterations. Accurate phenotyping of the patients and the collection of the sample material will promote the establishment of a biobank structure and networking with the specific sub-centers.


Selected publications

siehe Pubmed



E-Mail wagner.annette(at)