In medicine, preventive measures and the early identification of diseases often provide the best chances of recovery. One possible application is pulmonary embolism. Pulmonary embolism is a narrowing of a blood vessel in the lung or pulmonary circulation that can lead to a blockage. This is usually caused by a blood clot, or more rarely by gas bubbles or fat. As this condition is potentially life-threatening, it needs to be detected and treated very early on. EVA-KI aims to continuously collect data in clinical practice and to develop algorithms for clinical diagnoses from this data. In doing so, the AI should create a basis which enables a more efficient diagnosis of diseases and in the long term means that more treatment options are available.
The team with Professor Manuel Trenz, Professor for Interorganisational Information Systems at the University of Göttingen, focuses on the interactions with AI and its acceptance. "In addition to the technical and medical challenges, it is important to understand the interaction process between artificial intelligence and doctors and to design it in such a way that the new information can be used efficiently for diagnosis, ultimately improving medical care," explains Trenz. In fact, such innovations often fail to gain acceptance from decision-makers and, due to a lack of dissemination, can only have a limited impact. This is where the team’s work is essential as they can develop acceptance models as well as sustainable business models for the dissemination of AI solutions in the healthcare sector.
The interdisciplinary consortium with partners from computer science, medicine and information systems is co-funded by the Federal Ministry of Health (BMG) under the funding line Digitale Innovationen für die Verbesserung der patientenzentrierten Versorgung im Gesundheitswesen. Further information on the project can be found at http://hessian.ai-health.care/eva-ki.