Bo Wang

Bo Wang is tenure-track assistant professor of Department of Laboratory Medicine and Pathobiology and Department of Computer Science at the University of Toronto. He holds a CIFAR AI Chair at the Vector Institute. He also leads the AI team for the Peter Munk Cardiac Centre at the University Health Network. Bo Wang obtained his PhD from the Department of Computer Science at Stanford University and has extensive industrial research experience at many leading companies such as Illumina and Genentech. His PhD work covers statistical methods for solving problems in computational biology with an emphasis on integrative cancer analysis and single-cell analysis. Bo Wang’s long-term research goals aim to develop integrative and interpretable machine learning algorithms that can help clinicians with predictive models and decision support to tailor patients’ care to their unique clinical and genomic traits.

Keynote 1. Opportunities and challenges of artificial intelligence for organ transplantation

Organ transplantation is a life-saving procedure for patients with end-stage organ disease. A successful transplant depends on thorough pre-transplant evaluation and preparation, accurate identification of a suitable donor, and detailed follow-ups with post-transplant monitoring. There is no medical field where more variables are intercalated than in the case of a transplant patient. The embracement and integration of artificial intelligence with big data in such a setting are fundamental to improving access to transplantation, quality of life, and patient outcomes.

In this talk, I will discuss the opportunities and challenges of AI for organ transplantation. Specifically, I will first show a machine-learning model trained on hundreds of ex vivo lung perfusion (EVLP) cases, to accurately predict lung transplantation outcomes. Second, I will present a novel AI system that can seamlessly track longitudinal follow-up data to identify patients at increased risk for complications after liver transplantation. Last, current challenges to implementing AI in transplantation will be discussed.

David L. Buckeridge

David Buckeridge is a Professor in the School of Population and Global Health and the Chief Digital Health Officer at the McGill University Health Center. Holding a Canada Research Chair (Tier 1) in Health Informatics and Data Science, he projects health system demand for the Canadian province of Quebec, leads Data Management and Analytics for the Canadian Immunity Task Force, and supports the World Health Organization in monitoring global immunity to SARS-CoV-2. He has a MD (Queen's), a MSc in Epidemiology (Toronto), a PhD in Biomedical informatics (Stanford), and is a Fellow of the Royal College of Physicians of Canada.

Keynote 2. Translating AI into Practice in Healthcare – Opportunities, Challenges, and Possible Solutions

The potential for AI in healthcare has been evident for decades and the opportunity has grown with increasing volumes of data and advances in AI, particularly in machine learning. Despite this potential, the translation of AI-based innovations into healthcare practice has faced challenges along the development and implementation pipeline. These include access to data, technology debt in clinical practice, and AI expertise in healthcare systems.

In the presentation, I will present this landscape and identify possible solutions, drawing on examples from Canada and other countries.