Special issue: AUTOMED 2021
Automation in Medical Technology
- verfasst von
- Georg Rauter, Thomas Seel, Philipp Rostalski
- Abstract
Automation and the corresponding tools developed to design, analyze, and implement dynamical systems see widespread use in the medical domain. With the rise of digitalization and AI this development has gained significant additional momentum. In this special issue, a wide range of these technologies are exemplified in nine exciting contributions ranging from modelling to the development and testing of highly automated functions as well as the use of nonlinear state space models in machine learning.
This special issue is dedicated to the presentation of selected contributions from the regular interdisciplinary AUTOMED Symposium, which is organised by the Technical Committee for Automation in Medical Technology of the DGBMT/GMA of VDI/VDE (Fachausschuss Automatisierungstechnische Verfahren für die Medizintechnik der DGBMT/GMA im VDI/VDE). AUTOMED 2021 was hosted by the University of Basel (Switzerland) and, unfortunately, had to be held virtually due to the world-wide COVID 19 pandemic. Nevertheless, the symposium became a very successful scientific event, thanks to the support of an extremely committed and proactive AUTOMED community. This support allowed us to realize an interactive virtual two-days event with a total number of 36 high-quality peer-reviewed contributions. The best six out of all accepted contributions from AUTOMED 2021 were selected for inclusion in this special issue. Since the presentations of their work at the Symposium in June 2021, all authors largely extended their original contributions to full papers, which were then peer-reviewed for final acceptance in this special issue. In addition, we also included one free contribution, one invited contribution, and one summary of a PhD thesis in our special issue due to the articles’ closeness to the common topics discussed in AUTOMED.- Externe Organisation(en)
-
Universität Basel
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU Erlangen-Nürnberg)
Universität zu Lübeck
- Typ
- Editorial in Fachzeitschrift
- Journal
- At-Automatisierungstechnik
- Band
- 70
- Seiten
- 933-934
- Anzahl der Seiten
- 2
- ISSN
- 0178-2312
- Publikationsdatum
- 16.11.2022
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Steuerungs- und Systemtechnik, Angewandte Informatik, Elektrotechnik und Elektronik
- Elektronische Version(en)
-
https://doi.org/10.1515/auto-2022-0133 (Zugang:
Geschlossen)