Wiro Niessen

Bio Wiro Niessen is full professor in Biomedical Image Analysis and Machine Learning at Erasmus MC and Delft University of Technology, The Netherlands. His interest is in the development and validation of quantitative biomedical image analysis methods, and linking imaging and genetic data for improved disease diagnosis and prognosis, using machine learning (AI). He supervised 60 PhD students in these fields. He is Medical Delta professor (joint initiative of Erasmus MC, EUR, TU Delft, University Leiden and LUMC), and has led the "Health & Technology" Convergence Project Team to define the vision, mission and strategy of EMC, EUR and TU Delft to contribute to the future of health(care).

Wiro Niessen is fellow and was president of the MICCAI Society, and is CTO of Health-RI, which aims to develop a national health data infrastructure for reuse of data for research and innovation. In 2015 he received the Simon Stevin award, the largest prize in the Netherlands in Applied Sciences. In 2005 he was elected to the Dutch Young Academy and in 2017 he was elected to the Royal Netherlands Academy of Arts and Sciences. In 2012 he founded Quantib, an AI company in medical imaging where he currently acts as scientific lead. In 2022 Quantib has become part of the AI division of RadNet. Wiro Niessen is board member of NWO-TTW (2017-2023), and member of the SectorPlan Committee "Beta & Techniek". As of Feb 2023, he is dean of The Faculty of Medical Sciences, University Medical Center Groningen Summary The combination of big data and artificial intelligence are dramatically increasing the possibilities for prevention, cure and care, and have large potential to change the landscape of the healthcare system. However, the translation from promising research results towards successful and responsible implementation in clinical practice is challenging. In this presentation I will show the opportunities and challenges of big data analytics with AI techniques in health. Applications in the field of improved prognosis in dementia and improved diagnosis and prediction and oncology will be shown. Finally, I will address the challenges of how to successfully integrate these technologies in daily clinical workflow. This will require (i) good access to health data, both for the development of accurate and robust algorithms, and for their validation, and (ii) collaboration and co-creation within multidisciplinary teams consisting of academic researchers, clinicians and industry.