Our Vision

To implement Artificial Intelligence (AI) into transplant care.

With the confluence of AI expertise in Toronto and the largest transplant program in North America, we are working toward leading the world in AI tool development and implementing these tools in the clinical setting.

Our Goals

Our Transplant AI initiative is based on a solid foundation of clinical knowledge, computer science and data analytics and will facilitate advancements in the four major fields of interest:

Donor/recipient matching
  • Donor selection is a challenging and multifactorial decision influenced by both donor and recipient factors as well as match considerations.
  • Machine learning models can analyze a wide range of donor and recipient characteristics and detect complex nonlinear relationships between input variables to identify the most compatible matches.
  • We intend to use machine learning to optimize donor-recipient matching and increase post-transplant survival.
Prioritization of organ allocation
  • Allocation process is complex and involves many variables.
  • AI can help optimize the allocation process by uncovering non-linear and subtle correlations among these variables that cannot be identified using conventional analysis models.
  • AI has the potential to improve efficiency and fairness of organ allocation process.
  • AI can be used to develop predictive models that can identify patients who are at highest risk of mortality or dropout.

Long-term post-transplant complications
  • Long-term survival compromised by cardiovascular, cancer, infection and graft failure-associated mortality.
  • AI trained on large datasets can identify patterns and associations to predict outcomes.
  • Opportunity for individualized prevention or intervention plan for transplant recipients based on the top-ranked modifiable features.
Equity for organ allocation
  • Inequities in organ allocation due to various factors such as sex, gender, race, ethnicity, socioeconomic status, and access to healthcare.
  • AI could help address inequities in organ allocation process by enabling more accurate and efficient organ matching.

Our Core Values


Partner with patients, health teams, researchers and industry to improve the success of solid organ transplantation and the quality of life for transplant recipients.

Communication & Transparency

Raise awareness and share knowledge regarding transplant research and its advancements.

Research and Innovation

By leveraging AI technologies, we aim to optimize organ allocation, improve patient outcomes and increase the efficiency of the transplant process.


Guide the development and implementation of machine learning models and other AI techniques to improve outcomes for transplant patients.

Equity & Advocacy

Develop strategies to improve quality and quantity of life of our patients regardless of sex, race, gender, or other such factors and move towards a more equitable care.

Research Fellowship in Transplant AI

We are always open to applications from enthusiastic Transplant Clinical Research Fellows and Computer Science/Engineering graduate students/postdoctoral fellows wanting to do training in Transplant AI! As a leading centre in this area, you will have the unique opportunity to contribute to the development and deployment of machine learning tools into the transplant clinical setting.

Please send us your CV and letter of application to and if interested!

News & Events

Welcome to the 2023 Transplant AI Symposium: A Vision for the Future

Join us at our 2023 symposium, happening on November 27th both in person and virtually, where we will explore the future of transplantation and artificial intelligence. Our symposium brings together leading experts in the field to discuss the latest advancements and breakthroughs. Get ready to be inspired by thought-provoking talks, a panel discussion, and networking opportunities.

Learn more and register for the 2023 Transplant AI Symposium.

Our Team

Our team brings together a wide range of unique expertise in clinical care, research, and artificial intelligence.

Mamatha Bhat
Dr. Mamatha Bhat
Aman Sidhu
Dr. Aman Sidhu
Michael Brudno
Dr. Michael Brudno

Ghazal Azarfar
Ghazal Azarfar

Postdoctoral Fellow

Anirudh Gangadhar
Anirudh Gangadhar

Postdoctoral Fellow

Yingji Sun
Yingji Sun

Machine Learning Analyst

Sophie Liu
Sophie Liu

Software Developer

Peter Maksymowsky
Peter Maksymowsky

Data Engineer

Peter Bell
Dr. Peter Bell

Clinical Fellow

Supported by

UHN Foundation logo  
U of T logo  
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