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!


Explore our pioneering publications on leveraging Machine Learning and Artificial Intelligence in the transformative field of organ transplantation. Discover how these cutting-edge technologies are redefining possibilities, enhancing precision and improving outcomes in transplant medicine

News & Events

2023 Transplant AI Symposium: A Vision for the Future

We are immensely grateful to everyone who joined us for our recent symposium, where we explored the future of transplantation and artificial intelligence. Our event brought together leading experts in the field to discuss the latest advancements and breakthroughs. Your engagement and enthusiasm significantly contributed to the success of our symposium.

If you're interested in revisiting the insights shared during our event or if you missed any sessions, we're more than willing to share a copy of the recorded session. Please reach out to Elisa Pasini at to request your copy. We believe it will serve as a valuable resource for further reflection and insights.

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

Chief Data Scientist

Bo Wang
Dr. Bo Wang

Chief Artificial Intelligence (AI) Scientist

Elisa Pasini
Elisa Pasini

Program Manager

Yingji Sun
Yingji Sun

Machine Learning Analyst

Sophie Liu
Sophie Liu

Software Developer

Peter Maksymowsky
Peter Maksymowsky

Data Engineer

Rahul G. Krishnan
Dr. Rahul G. Krishnan

Assistant Professor, Dept. of Computer Science

Ghazal Azarfar
Ghazal Azarfar

Postdoctoral Fellow

Anirudh Gangadhar
Anirudh Gangadhar

Postdoctoral Fellow

Xun Zhao
Dr. Xun Zhao

Clinical Fellow

Working Group

  • Dr. Mamatha Bhat
  • Dr. Michael Brudno
  • Dr. Alba Carolina
  • Dr. Sasan Hosseini
  • Dr Shahid Husseini
  • Dr. Elmar Jaeckel
  • Dr. Kim Joseph
  • Dr. Shaf Keshavjee
  • Dr. Rahul G. Krishnan
  • Dr. Yas Moayedi
  • Dr. Istvan Mucsi
  • Dr. Andrew Sage
  • Dr. Gonzalo Sapisochin
  • Dr. Aman Sidhu
  • Dr. Tom Waddell
  • Dr. Bo Wang

Supported by

UHN Foundation logo  
U of T logo  

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