AI Rounds

Artificial Intelligence for Augmentation of Surgical Performance: Promises, Perils and Realistic Expectations

Speaker: Dr. Amin Madani

• Staff Surgeon, Department of Surgery, University Health Network​

• Assistant Professor, Department of Surgery, University of Toronto​

• Founder and Director, Surgical Artificial Intelligence Research Academy (SARA), University Health Network​

• Chair, Surgical Data Science Committee / Task Force, Society of American Gastrointestinal and Endoscopic Surgeons (SAGES)​

• Co-founder and Chair, Global Surgical AI Collaborative (GSAC)

Session Description:

In this presentation, Dr. Amin Madani explores how deep learning for computer vision can be used to provide real-time guidance to surgeons and augment their performance. He also discusses the burgeoning field of surgical data science, including the many applications of machine learning for improving patient care throughout the many phases of their journey in the health care system. Finally, he discusses the many pitfalls and obstacles to develop and implement clinically-relevant solutions that will have a real and meaningful impact on patients.
Forthcoming UHN-wide initiatives, such as the Mayo Clinic Platform and efforts to democratize access to data across UHN.

AI Rounds: Getting the most out of UHN DATA with AI

Speaker: Michael Brudno

• Chief Data Scientist, University Health Network (UHN)

• Professor in the Department of Computer Science at the University of Toronto

• Faculty Member and CIFAR Chair at the Vector Institute

This talk covered:

• AI projects undertaken by the UHN DATA Team, including the deployment of Large Language Models for analysis of patient notes and improving the workflows of clinics by using AI.
• Forthcoming UHN-wide initiatives, such as the Mayo Clinic Platform and efforts to democratize access to data across UHN.

Foundation Models for Biomedical Image Analysis

Speaker: Jun Ma, PhD
Machine Learning Lead at UHN AI Hub

Foundation models are reshaping biomedical image analysis by enabling generalizable and multimodal solutions across diverse tasks. This talk will cover foundation models for general biomedical image segmentation across diverse modalities, multimodal large language models for joint image–text understanding, generative model for understanding cellular responses under various perturbations, and medical agents that integrate vision and language capabilities for interactive analysis. These approaches demonstrate how foundation models can accelerate research and expand the toolset available to the biomedical imaging community.

Leveraging LLMs for Clinical Impact Today

Speaker: Dr. Rajesh Bhayana

• Radiologist, University Medical Imaging Toronto (UMIT)
• Technology Lead, Joint Department of Medical Imaging (JDMI)
• Assistant Professor, University of Toronto

Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care through Education and Knowledge Exchange

Speaker: David Wiljer PhD

• Executive Director, Education, Technology & Innovation, University Health Network
• Collaborator Scientist, Centre for Addiction and Mental Health
• Director of Curriculum, Medical Psychiatry Collaborative Care Certificate (MP3C Program), Trillium Health Partners, Ontario, Canada
• Professor, Psychiatry, Faculty of Medicine, University of Toronto
• Professor, Radiation Oncology, Faculty of Medicine, University of Toronto

Date and Time: Wednesday February 7, 2024, 5:00-6:00pm

Implementing AI in the Clinic

Speaker: Andrew Hope, MD, FRCPC

• Radiation Oncologist, UHN
• Associate Professor, Department of Radiation Oncology, University of Toronto
• Associate Member, Institute of Medical Science, University of Toronto

Applied Artificial Intelligence in Health: Case Examples and Learnings

Speaker: Muhammad Mamdani PharmD, MA, MPH

• Vice President, Data Science and Advanced Analytics, Unity Health Toronto
• Odette Chair in Advanced Analytics
• Faculty Affiliate, Vector Institute
• Director, University of Toronto Temerty Centre for Artificial
• Intelligence Research and Education in Medicine (T-CAIREM
• Professor, University of Toronto

Surgeons, Organs, and Data – How Machine Learning is Transforming Transplant Medicine

Speaker: Dr. Andrew Sage

• Assistant Scientist, Toronto General Hospital Research Institute
• Toronto Lung Transplant Program, University Health Network
• Assistant Professor, Department of Surgery
• Temerty Faculty of Medicine, University of Toronto

The future is here: What is ChatGPT and how it can transform healthcare.

Speaker: Dr. Bo Wang

• CCAI Chair, Vector Institute
• AI Lead, University Health Network
• Assistant Professor, University of Toronto