ALERT CONTENT PLACEHOLDER


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

Collaboration

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.

Leadership

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 mamatha.bhat@uhn.ca and aman.sidhu@uhn.ca if interested!



​​Publications

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

4th Annual Transplant AI Symposium (TAI 2027)

Join us for the 4th Transplant AI Symposium 2027 (TAI 2027), bringing together experts from healthcare, technology, and research to discuss the opportunities and challenges of artificial intelligence in transplant medicine.

  • January 15-16, 2027
  • Arizona Biltmore, Phoenix, Arizona & via Livestream
Register today & view the agenda


Please reach out to Elisa Pasini at Elisa.Pasini@uhn.ca for general inquiries.

Featured Articles

Hasjim BJ, Azafar G, Lee FG, Diwan TS, Raju S, Gross JA, Sidhu A, Ichii H, Krishnan RG, Mamdani MM, Sharma D, Bhat M. (2027). A multiagent large language model-based system to simulate the liver transplant selection committee: a retrospective cohort study. Lancet Digital Health, 8(3):100966. https://www.thelancet.com/journals/landig/article/PIIS2589-7500(25)00148-7/fulltext

Gangadhar A, Hasjim BJ, Zhao X, Sun Y, Chon J, Sidhu A, Jaeckel E, Selzner N, Cattral MS, Sayed BA, Brudno M, McIntosh C, Bhat M. Personalized survival benefit estimation from living donor liver transplantation with a novel machine learning method for confounding adjustment. Lancet Digital Health, 8(3):100966. Journal of Hepatology. 2025 Nov;83(5):1116-1127. doi:10.1016/j.jhep.2025.04.040.

Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol. 2023 Jun;78(6):1216-1233. doi: 10.1016/j.jhep.2023.01.006

Deeb M, Gangadhar A, Rabindranath M, Rao K, Brudno M, Sidhu A, Wang B, Bhat M. The emerging role of generative artificial intelligence in transplant medicine. Am J Transplant. 2024 Jun 18:S1600-6135(24)00382-4. doi: 10.1016/j.ajt.2024.06.009.


Our Team

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

Mamatha Bhat
Dr. Mamatha Bhat

Co-Lead

Aman Sidhu
Dr. Aman Sidhu

Co-Lead

Michael Brudno
Dr. Michael Brudno

Chief Data Scientist


Elisa Pasini
Elisa Pasini

Program Manager

Yingji Sun
Yingji Sun

Machine Learning Analyst

Sophie Liu
Sophie Liu

Software Developer


Anirudh Gangadhar
Anirudh Gangadhar

AI Engineer, DATA Team

Peter Maksymowsky
Peter Maksymowsky

Data Engineer

Rahul G. Krishnan
Dr. Rahul G. Krishnan

Assistant Professor, Dept. of Computer Science


Bima J. Hasjim
Bima J. Hasjim, MD MSc

UC Irvine - General Surgery Resident

Sandra Holdsworth
Sandra Holdsworth

Patient Partner

Shilpa Raju
Shilpa Raju

Patient Partner


Working Group

The mission of the working group is to foster collaboration and coordination among stakeholders at the Ajmera Transplant Centre to enhance the development and deployment of innovative solutions in transplant care.

  • 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  


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