Session
NASK PIB National Research Institute, Poland
Ilona Urbaniak, Ph.D.
Head of Artificial Intelligence Department, NASK PIB National Research Institute, Poland
Moderator: Ilona Urbaniak, Ph.D., M.Sc. (NASK, Poland)
Head of Artificial Intelligence Department
Artificial Intelligence Data Analytics Department, AIDA
Research and Academic Computer Network NASK PIB
Assistant Professor
Faculty of Computer Science and Telecommunications
Cracow University of Technology
Speaker: Dr. David Koff, MD FRCPC FCAR (Canada)
Professor Emeritus
Department of Radiology, McMaster University
Director MIIRCAM
Chair Canada Safe Imaging
Speaker: Prof. Bruce Spencer, Ph.D., M.Math (Canada)
Senior Research Officer / Data Analytics / Digital Technologies Research Centre
National Research Council Canada
Adjunct Professor UNB
Speaker: Davide La Torre (France)
Full Professor of Artificial Intelligence and Applied Mathematics
Director of the SKEMA Artificial Intelligence Institute
Head of the Programme Grande Ecole Track in Artificial Intelligence for Managers
Head of the Programme Grande Ecole Track in Finance and Quants
SKEMA Business School, Université Côte d'Azur
Speaker: Bartosz Borucki (Poland)
University of Warsaw
Interdisciplinary Centre of Mathematical and Computational Modelling
Head of Artificial Intelligence and Image Analysis in Medical Diagnostics group
Smarter Diagnostics, CEO
Speaker: Ewa Kawiak-Jawor (Poland)
Center for Technology Assessment
Łukasiewicz Research Network
Institute of Organization and Management in Industry
Ilona Urbaniak
Gathering
The challenges in making medical data public have become increasingly important for advancing data-driven biomedical research. The factors that have hindered the use of AI in healthcare include data access, bias, transparency, and integration. These issues must be addressed to ensure the successful use of AI in healthcare. Medical data standards include specifications methods and protocols for information exchange, storage, retrieval associated with medical records. Enhanced access to medical data and the development of industry standards may help leaders eliminate potential biases in AI healthcare tools, as well as improve technology implementation. The main goal is to minimize the risk of disclosure with proper data protection such as anonymization while respecting applicable legal and ethical guidelines. An important topic of discussion is federated learning/analytics vs. anonymization in the context of medical data.
The moderator and the speakers will be able to interact and participate using the official online participation platform provided by IGF.