Case Study: Medical University of Vienna achieves faster, less-invasive tumour characterisation with Microsoft Azure

The Researchers Saving Priceless time for tumor patients with AI

The Medical University of Vienna, one of Europe’s leading medical research and education institutions, confronted a major challenge: tumour characterisation relied on slow, invasive biopsies and outdated processes while other imaging fields adopted AI. A research team from the Quantitative Imaging and Medical Physics group and the Division of Nuclear Medicine aimed to develop non‑invasive, predictive methods using hybrid anato‑metabolic imaging (e.g., PET/CT) to make tumour diagnosis faster, more accurate and less distressing for patients.

Working with Microsoft, the team used Azure infrastructure and Cognitive Services to train AI models on large-scale imaging data, enabling rapid, in‑depth analysis of tumours at the point of scan. The approach is accelerating research and clinical decision support, improving diagnostic speed and patient experience, reducing reliance on biopsies, and laying the groundwork for better outcomes and long‑term cost savings.

Medical University of Vienna

Thomas Beyer

Medical Physicist, Medical Physics & Biomedical Engineering and Division of Nuclear Medicine Research team