(Vienna, 04 September 2025) Philipp Tschandl, research group and laboratory head at the Department of Dermatology at MedUni Vienna, has been awarded a Starting Grant from the European Research Council (ERC) worth €1.5 million over five years. The aim of the project "Automating knowledge extraction from large dermatopathology datasets to establish objective and accessible diagnostic frameworks" (AUTODIAL) is to use machine learning to extract knowledge from unstructured medical data and gain a better understanding of diagnostic grey areas.
The ever-growing mass of data in health and science, including image collections, textbooks and scientific literature, makes it increasingly difficult, and in some cases even impossible, to meaningfully capture, validate or query the current state of knowledge. However, modern developments in machine learning offer promising solutions to these challenges by enabling the collection, understanding, curation, use and interaction with large amounts of data. The aim of the AUTODIAL project is to develop such techniques in the context of a specialised medical diagnostic field, dermatopathology. "Extracting knowledge from large unstructured data can enable us to understand and objectively present connections between diseases, treatment courses and misdiagnoses," explains Tschandl. "I hope that reliable text and image analysis methods will improve data quality in the healthcare sector in the long term and, as a 'side effect', perhaps also relieve medical staff of documentation tasks in the long run."
About
Philipp Tschandl studied human medicine and medical sciences as part of the MDPhD Excellence Programme at the Medical University of Vienna. After completing his medical studies, he trained as a specialist in dermatology with a focus on dermatopathology at the University Clinic for Dermatology and the Clinical Institute for Pathology. Philipp Tschandl spent time abroad at Simon Fraser University (CAN), the University of Queensland (AUS) and the Ackerman Academy of Dermatopathology (USA). His research focuses on diagnostic imaging, machine learning and medical data sets in the context of dermatology.