Applied AI in Healthcare (AAI)

Bild Applied AI

Our research group “Applied Artificial Intelligence in Healthcare” develops AI-based methods that make complex temporal and spatial data usable for medicine. Our aim is to help improve the understanding of diseases and their treatment. Our data range from continuous sensor signals from wearables in the home environment to environmental influences and structured routine data from clinical information systems. In addition, we use interoperable data sources from multiple sites to analyze clinical data across institutions and to address population-based research questions ranging from the distribution of socioeconomic risk factors to the validation of predictive models using registry data. As an interdisciplinary research group, we work at the intersection of medical informatics, cognitive science, data science, and epidemiology.

Methodologically, we focus on symbolic and sub-symbolic artificial intelligence, time-series-based machine learning, synthetic data generation, and quality assurance of AI systems in the clinical context, particularly with regard to their generalizability. A central concern is translation: we develop algorithms and models with the goal of later transferring them into practical applications, while taking into account the regulatory requirements of medical device law and the EU AI Act.

We always welcome collaboration requests from clinical practice, research, and industry. We are also very happy to supervise early-career researchers, for example in the context of master's theses, student research projects, or medical doctoral theses.

Publications

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Current projects

  • Cap
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    A Cap for Detecting Hypomimia

    Parkinson's Disease (PD) is an incurable neurodegenerative disorder characterized by the loss of dopamine-producing cells in the substantia nigra. The resulting symptoms can significantly impact patients' quality of life. Current wearable technology is capable of identifying motor symptoms like tremor, but largely unexplored symptoms such as hypomimia, a reduction in facial expressiveness, remain difficult to assess. Present methods, such as in-clinic video recordings, do not allow for continuous monitoring in daily life, which is crucial for understanding disease progression and adjusting treatment strategies. Our team, in collaboration with the Movement Disorders and Deep Brain Stimulation Working Group led by PD Dr. med. Monika Pötter-Nerger, has developed and prototyped an innovative measurement instrument. This device can precisely monitor subtle facial muscle movements and wirelessly transmit the data to an iOS application. We are currently seeking dedicated medical doctoral candidates for the clinical management and initial exploratory analyses of this project. Interested individuals are encouraged to contact us via email. For further information, Christopher Gundler and Alexander Wiederhold are available as contacts.

  • Internet of Things and Parkinson's disease

    To improve the quality of life for patients with Parkinson's disease, inpatient examinations and treatments are often conducted. During hospital stays, adjustments to the current treatment plan are prioritized. Accurate and personalized treatment is crucial for therapeutic success.

    With the help of a specialized tablet application, patients have the opportunity to independently record relevant parameters of their current well-being and other non-motor symptoms. Similar to a digital diary, this allows for the collection of a wide range of disease-relevant data. This is complemented by the use of sensors that communicate with the tablet through locally networked systems, enabling the tracking of key vital parameters over time.

    Through the use of these connected systems, we hope to gain new insights into the severity of Parkinson's disease symptoms. In a previous project, we have already collected comparable data as well as preliminary movement data and medication intake.