AI in
Medicine
Exploring the potential of artificial intelligence for data analysis, personalised treatment, and ethical standards in healthcare.
Artificial intelligence (AI) is a transformative technology in medicine for the analysis of large datasets and the automation of complex processes. One area of application, for example, is the analysis of data from wearable sensors using AI methods in geriatric rehabilitation — identifying and analysing complex movement patterns, personalising treatment approaches, and measuring treatment outcomes. At the same time, important questions arise around data protection, ethical standards, and the legal framework. In our interdisciplinary team, we investigate the various dimensions and possibilities of deploying AI in medicine.
Prof. Dr. Alexandra Jorzig
alexandra.jorzig@hi-university.deProf. Dr. Winfried Graf
winfried.graf@hi-university.deProf. Dr. Alexandra Jorzig
alexandra.jorzig@hi-university.deProf. Dr. Jochen Klenk
jochen.klenk@hi-university.deProf. Dr. Philipp Lacour
philipp.lacour@hi-university.deProf. Dr. Ingo Schmehl
ingo.schmehl@hi-university.deApl. Prof. (Uni Hamburg), Dr. Jin Yamamura
jin.yamamura@hi-university.de
AktiSmart-KI (BMG, 04/2020–9/2022)
As part of this collaborative project with the University of Ulm and the Robert Bosch Hospital Stuttgart, the low-threshold use of body-worn activity sensors in geriatric rehabilitation was investigated, with the aim of enabling the automated analysis of complex movement patterns through AI methods. To achieve this goal, three sub-objectives were pursued by an interdisciplinary team of computer scientists, physicians, legal scholars, psychologists, and ethicists: (1) developing the technology for learning from body-worn sensor data, (2) analysing the framework conditions for clinical deployment with a focus on data protection as well as ethical and legal considerations, and (3) piloting the approach under real-world clinical conditions.
Ajlani, A.; Klenk, J.; Lindemann, U., et al (2023): User Perspectives of Geriatric German Patients on Smart Sensor Technology in Healthcare. Sensors 23:9124.
https://www.mdpi.com/1424-8220/23/22/9124Becker, C.; Bourke, AK.; Klenk, J., et al. (2021): Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping. Sensors 21:2601.
https://www.mdpi.com/1424-8220/21/8/2601