A central focus of the AI Innovation Center is direct cooperation with industrial companies: Feasibility studies (Quick Checks) and projects to develop initial prototypes (Exploring Projects) of AI applications. In addition, consortium projects and innovation networks are aimed at cross-company exchange.
Teaching faults via a single sensor on the bike offers the opportunity to increase the quality of bikes, service and safety.
An AI-supported system for extracting, summarizing and translating medical publications is being developed to make specialist knowledge more efficiently accessible and to cope with the flood of information.
The analysis of the patient journey at Klinikum Stuttgart identifies AI applications to optimize care, increase employee satisfaction and improve economic and ecological criteria.
It was evaluated how the preparation of marketing authorization dossiers (core dossiers) can be supported by the extraction of information from medical studies.
Algorithms learn from existing production data and CAD geometries, estimate component complexity in real time, improve cost transparency, automate costing and relieve teams of manual assessments and time-consuming customer communication on a day-to-day basis.
The AI Innovation Center's intuitive label editor optimizes the labeling process through structured workflows and automation features, increasing efficiency and ease of use.
An AI-supported sales forecast analyzes historical sales data and external factors to predict future sales figures more accurately and enable data-based decisions.
Research on AI anomaly detection with limited data sets aims to develop and test efficient methods for identifying anomalies in various industries despite data scarcity.
In the sharing economy, efficient returns pose a major challenge. This can be largely automated through the use of AI.
The project developed an AI-based, reliable and fully automated solution for print image monitoring. This reduces false alarms by an average of 85.7% through anomaly detection.