WG 6: Health Care, Medical Technology, Care

Self-learning systems promise a great potential for the health care system. Prevention, diagnosis and therapy are improved through the use of intelligently linked patient data. Medical assistance systems will help to recognise diseases at an early stage. They will also assist during surgery. In the care sector, digital assistances can help lifting and moving older patients. It is not the goal of medical AI-applications to replace skilled workers. Instead, the new technology wants to unburden and to support as best as possible. At the same time, the health care system is confronted with several important social questions. For instance, how safe is the personal health data being protected? Who is liable when the AI misdiagnosis?

Topics and organisation of the Working Group

The Working Group deals with the options provided by self-learning systems for prevention, diagnosis and treatment in the medical field, in long-term care and rehabilitation. It also addresses questions of social acceptance and data protection.

Karsten Hiltawsky

Klemens Budde

Members of the Working Group

Dr. Jean-Enno Charton
Merck KGaA

Prof. Dr. Roland Eils
Berlin Institute of Health (BIH)

Prof. Dr. Björn Eskofier
Friedrich-Alexander-Universität Erlangen-Nürnberg

Dr. Björn Heismann
Siemens Healthineers AG

Prof. Dr. Dagmar Krefting
University Medical Center Göttingen (UMG)

Prof. Prof. h. c. Dr. med. Thomas Lenarz
Hannover Medical School (MHH)

Dr. Wiebke Löbker
Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM)

Prof. Dr. Wolfgang Nejdl
Leibniz University Hannover, L3S Research Center

Prof. Dr. Thomas Neumuth
Leipzig University, Innovation Center Computer Assisted Surgery (ICCAS)

Dr. Abtin Rad
TÜV SÜD Product Service GmbH

Dr.-Ing. Matthieu-P. Schapranow
Hasso Plattner Institute for Digital Engineering

Andrea Schmidt-Rumposch
University Hospital Essen

Steffi Suchant
Techniker Krankenkasse

Barbara Susec
ver.di

Eva Maria Welskop-Deffaa
Deutscher Caritasverband e.V.

Prof. Dr. Karin Wolf-Ostermann
University of Bremen

Prof. Dr. Ing. Thomas P. Zahn
bbw Hochschule

We have received written consent from the listed persons for the publication of their data in accordance with the DSGVO. This list of members is an excerpt and will be completed continuously.

Key Questions for the Working Group

Which opportunities occur for self-learning systems when patient, clinical and wearable data is connected with each other?
How can we use Data Science and AI for prevention approaches?
What are the opportunities for self-learning assistant systems during operations and in the care sector?
Which role do AI-technologies play when it comes to prosthetics and exoskeletons?
How can self-learning systems relieve skilled personal in health care?

Conclusions and contributions of the Working Group

A comprehensive overview of all results and contributions of the working group available in German can be found here.

Publications
Expertise
Videos (German)