9 Symposium 2022 Curriculum Vitae: After completing her degree in Medicine, Prof. Woitek carried out speciality training in Radiology at the Medical University of Vienna from 2008–2014. She subsequently went on to write her PhD entitled “Fetal MRI in the (micro-) structural and functional assessment of the posterior fossa”. Between 2014 and 2017 she was Postdoctoral Fellow and Consultant Radiologist at the Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna. She was awarded the Erwin Schrödinger Research Fellowship at the University of Cambridge in 2017 after which she was appointed Senior Research Associate and Honorary Consultant Radiologist at the Department of Radiology, University of Cambridge. Since 2022 she is Professor of Radiology and Head of the Research Group Medical Image Analysis and AI (MIAAI) at the Danube Private University (DPU) in Krems, Austria. Prof. Woitek is Author of 56 peer-reviewed scientific publications. Abstract: Get to know MIAAI The audience will be indroduced to the research group MIAAI (Medical Image Analysis and AI), its members and main areas of work and will be taken on a tour through the field of radiomics research. Radiomics allows the characterisation of tumours using large numbers of quantitative features extracted from medical images (such as CT, MRI and mammograms) and thereby strongly increases the information gained from these images when compared to clinical image interpretation by radiologists. Artificial intelligence (AI) allows the development of radiomics-based prediction tools for patient outcome, such as survival or treatment response which can support clinicians and patients in their decision making. Radiomics are gaining immense interest, but profound understanding of the methodological background and risks for bias are crucial and MIAAI is working towards improvements in both areas. Besides maximizing the utility of standard clinical imaging, MIAAI is also exploring advanced MRI techniques, such as sodium and hyperpolarised carbon-13 MRI, and their potential for very early response assessment in patients with breast cancer to spare patients from the side effects of potentially non-efficacious treatments. Novel techniques and AI-based tools in particular can be a reason for concern on the one hand, but are also celebrated for their potential to reduce our clinicians’ workload. MIAAI is interested in understanding the expectations towards AI among different members of a healthcare team and to see if improved knowledge transfer can positively affect these expectations. Univ.-Prof. DDr. Ramona Woitek Head of Research Group, MIAAI, Danube Private University, Krems, Austria © Cathrin Andel
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