PROJECT "ZIEL"
Reduced Time to Healing Through Enhanced Evidence with Augmented Intelligence
In collaboration with
Supported by
The ZIEL project primarily addresses the question: How can a suitable human-AI interface increase trust and acceptance in medical data-driven decision support, and how can critical engagement with it be achieved? Additionally, it seeks to answer a secondary, application-focused question: Which therapeutic approaches are associated with reduced healing times for chronic wounds in specific disease scenarios (evidence), and what decisions can be recommended based on this?
The complex interprofessional care of individuals with chronic wounds serves as an excellent practical example of the opportunities and pitfalls of data-driven care support within a "learning health system." Analyzing large volumes of routine data (diagnostics, therapy) on the disease progress of patients with chronic wounds or related risks provides the opportunity to develop prognostic models for physicians and nurses, and also to create educational opportunities for patients.
It is crucial to provide them with sufficient insight and intervention capabilities into the mechanisms of decision-supporting systems to build the success factor "trust." Key elements include simulating typical wound images based on specific queries (e.g., "show me a purulent wound"), visualizing healing parameters over time, presenting the confidence of predictions, and integrating this information into a dashboard as a human-AI interface for intelligent interaction and trust-building.