Speaker: Dr. Nils Daniel Forkert, PhD
About the talk: Artificial intelligence (AI) is expected to be a key tool for converting big data into tangible benefits in this increasingly data-driven world. Healthcare is no exception to this. The increased availability of diagnostic tools and techniques has increased the amount of medical data acquired and available for a single patient as well as at the population level. However, the sheer amount of data can be challenging and time-consuming to analyze. Supervised machine learning (ML) has great potential to augment clinical decision-making, alleviate the clinical burden, and promote precision in healthcare delivery. Despite the promise of ML to improve patient care and support medical doctors with complicated cases, the reality is that only a limited number of methods developed in the research domain are available and approved for clinical use today. In this talk, I will discuss some of the main barriers preventing a clinical application of AI for routine clinical use and present promising solutions to overcome current problems.
About the Speaker: Dr. Nils Daniel Forkert, PhD (h-index 49, citations 8900), is a Professor at the University of Calgary in the Departments of Radiology, Clinical Neurosciences, and Electrical and Software Engineering. He received his German diploma in Computer Science in 2009 from the University of Hamburg, his master’s degree in medical physics in 2012 from the Technical University of Kaiserslautern, his PhD in computer science in 2013 from the University of Hamburg, and completed a postdoctoral fellowship at Stanford University before joining the University of Calgary as an Assistant Professor in 2014. He is an imaging and machine learning scientist who develops new image processing methods, predictive algorithms, and software tools for the analysis of medical data. This includes the extraction of clinically relevant parameters and biomarkers from medical data describing the morphology and function of organs with the aim of supporting clinical studies and preclinical research as well as developing computer-aided diagnosis and patient-specific, precision-medicine, prediction models using machine learning based on multi-modal medical data. Dr. Forkert is a Canada Research Chair (Tier 2) in Medical Image Analysis, and Director of the Child Health Data Science Program of the Alberta Children's Hospital Research Institute as well as the Theme Lead for Machine Learning in Neuroscience of the Hotchkiss Brain Institute at the University of Calgary. He has published over 230 peer-reviewed manuscripts, over 100 full-length proceedings papers, 1 book, and 2 book chapters. He has received major funding from the Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council, the Heart and Stroke Foundation, Calgary Foundation, and the National Institutes of Health as a PI or co-PI. He currently supervises six postdoctoral fellows, two PhD students, and seven MSc students demonstrating his dedication to training the next generation of data science researchers.