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 embrace and integration of artificial intelligence with big data in this 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.
Biography
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.
Affiliations:
• University of Toronto
• University Health Network
• Vector Institute
Learning Objectives
• Participants will be able to understand their role in using AI to improve patient care and experience.
• Participants will be able to understand the benefits and limitations of AI technology.
• Participants will be able to understand the challenges associated with AI including ethical and security concerns.
• Participants will be able to understand the differences between artificial intelligence, machine learning, neural networks, and deep learning.
• Participants will be able to understand the requirements of deploying AI technology in the healthcare setting (such as data access).
