Industrial Domains

Healthcare

The Healthcare industrial domain seeks to address the need for integrating AI technologies to improve efficiency, accuracy, and patient outcomes, while tackling challenges such as rising costs, workforce shortages, and increasing patient volumes. The main challenge is integrating AI into existing clinical workflows and electronic health record systems, addressing interoperability, data privacy, and ethical concerns. Careful validation of AI algorithms and strict adherence to regulatory frameworks are essential to ensure equitable healthcare delivery. Overcoming these challenges will allow AI to enhance clinical decision-making and transform healthcare in a responsible and sustainable manner. 

Sub-Domains
Topics
Projects
Selected project: Gecko: VR Healthtech App for AI driven Cardio Fitness Coach 
Third parties involved: Carmen Mac Williams, Rene Vogels, Erik Hermann – Grassroot Arts and Research UG. 

The use of AI and wearables to unobtrusively monitor Parkinson’s Disease (PD) symptoms, enabling clinicians to offer personalized treatment recommendations. The focus is on detecting symptoms like tremors, bradykinesia, and dyskinesia. 
This project aims to develop a VR health technology prototype featuring an AI-driven Dance Fitness Coach designed to support patients with chronic cardiovascular disease by promoting physical activity and pain relief through interactive social dancing anytime, anywhere. Scientific evidence shows that dancing improves pain management, emotional well-being, mobility, and fosters social interaction. Codenamed Gecko—will collaborate with cardio physiotherapists from Riehler Gesundheitszentrum in Cologne, Germany, to train the AI agent. No clinical trials with patients will be conducted at this stage. Instead, the focus will be on developing a next-generation AI cardio fitness coach prototype integrated into GAR’s existing “Magic Plant World” VR environment, previously validated in lab tests. Through Gecko, GAR aims to redefine digital health, offering an innovative AI/VR approach to cardiovascular fitness and pain relief that goes far beyond conventional cardio exercise apps.

Selected project: Generative LLM for Single-Cell Transcriptomics of Alzheimer’s Disease 
Third parties involved: Siozos Theodoros, Petrou Christos, Balomenos Athanasios, Kopsinis Yannis – LIBRA AI Technologies 
Alzheimer’s disease (AD) is characterized by amyloid plaques and tau tangles, leading to brain degeneration. Traditional bulk sequencing fails to capture the cellular diversity of the CNS, but single-cell sequencing provides granular insights into individual cells. The proposed scGPT model, pre-trained on over 10 million cells, utilizes Transformers to enhance tasks such as batch correction, cell type annotation and gene network inference. The specialized scGPT-AD model aims to integrate scRNA-seq with other omics data to identify cellular perturbations and biomarkers. This approach supports personalized medicine by tailoring treatments based on individual genetic profiles, advancing our understanding and management of AD.

Selected project: Explainable association of drug side effects to changes in the proteome  
Third parties involved: András Ecker, Írisz Mertus, Gergely Szabó and Zsófia Binder – Cytocast 

This project develops explainable machine-learning models that connect drug-induced changes in protein complexes to human side effects, using Cytocast’s Digital Twin Cell™ simulations across multiple human tissue types. By simulating hundreds of drugs and training models on the resulting interaction patterns, we aim to deliver biologically grounded explanations of safety risks. The outcome will enable European pharmaceutical companies—large and small—to perform in-silico safety testing of compounds, accelerating drug development while improving interpretability and confidence in preclinical decisions.
Project: Risk Assessment and Decision Support for ICU Readmission Prediction

Third parties involved

This project develops an ensemble of deep learning (DL) predictive models to assess ICU readmission risk using MIMIC-IV and eICU data. We will review multimodal DL models relevant to decision support, train models on MIMIC-IV, fine-tune them on eICU via transfer learning, and combine their predictions with Bayesian Model Averaging to achieve probabilistic forecasting for clinical decision-making.