The third annual International Conference on Aging, Innovation, and Rehabilitation (ICAIR), hosted by UHN’s KITE Research Institute, took place on May 1 and 2, 2025 at The Carlu in downtown Toronto.
The conference highlighted the latest innovations in aging and rehabilitation technology, showcasing advancements designed to improve the lives of individuals living with disabilities, injuries, and the effects of aging. This year’s event welcomed 500 attendees from around the world, fostering cross-disciplinary collaboration and the exchange of research and academic insights.
Keynote speakers delivered engaging and thought-provoking presentations that explored the future of technology, health care, and social impact. Featured speakers included the following:
● Dr. Helen Papagiannis, globally recognized expert on immersive technologies and bestselling author of Augmented Human: How Technology is Shaping the New Reality.
● Dr. Chet Moritz, Professor and Co-Director for the Center for Neurotechnology at the University of Washington.
● Dr. Andrew Boozary, Clinician Investigator at the Toronto General Hospital Research Institute, primary care physician, and founding executive director of the Gattuso Centre for Social Medicine at UHN.
The program also featured notable events, including the KITE Power Play Pitch Competition, the Three-Minute Talk (3MT) Competition, and the ICAIR Scientific Spotlight Series. These sessions provided a platform for emerging researchers and innovators to present their work and engage with peers across disciplines.
Learn more about the competitions and their winners here.
For thousands of liver transplant recipients worldwide, routine follow-up can quickly turn uncertain when liver enzyme levels rise—an early signal of potential graft injury. The diagnostic gold standard, a liver biopsy, is invasive and often delayed. Additionally, physicians may adjust management and immunosuppression based on intuition, sometimes before receiving biopsy results, which can lead to complications. A new AI-driven tool called GraftIQ provides a safer approach to the early management of patients with elevated liver enzymes.
Developed at the University Health Network (UHN), GraftIQ is a first-of-its-kind hybrid model that blends clinician expertise with machine learning to provide a multi-class prediction, meaning it can distinguish between several causes of liver graft injury. This “human-in-the-loop” approach enables the system to learn from both data and domain knowledge, thereby enhancing its ability to detect six major causes of graft injury.
Trained and validated on more than 8,000 biopsies across three continents, GraftIQ consistently outperformed traditional models, showing strong generalizability and clinical relevance. In trials, it helped identify conditions like acute cellular rejection, biliary obstruction, and recurrent hepatitis C with high accuracy—without the need for invasive testing.
Importantly, international partners collaborated on the external validation, including Drs. Joseph Ahn (Mayo Clinic, USA), Richard Taubert (Hannover Medical School, Germany), and Eunice Tan (National University Health System, Singapore). GraftIQ performed strongly in these validations, demonstrating a level of generalizability across three continents that is rare for health care AI tools.
“This kind of technology does not replace clinical judgment—it enhances it,” says Dr. Mamatha Bhat, Hepatologist and Co-Lead of the Transplant AI initiative and Scientist at UHN, who envisioned and led this project. “GraftIQ is a step toward faster diagnosis and more timely treatment decisions.”
By integrating seamlessly into clinical workflows, GraftIQ represents a scalable, multi-class decision-support tool for transplant programs worldwide. Its success underscores the value of global collaboration in evaluating health care AI tools. UHN stands at the forefront of applying responsible AI in medicine.
Dr. Divya Sharma, Senior Biostatistician at Princess Margaret Cancer Centre, and Assistant Professor in the Department of Mathematics and Statistics at York University, is co-first author of the study.
Dr. Neta Gotlieb, from the Department of Medicine at the University of Ottawa and Dr. Daljeet Chahal, from the Vancouver General Hospital, are co-first authors of the study.
Dr. Wei Xu, Clinician Scientist at the Princess Margaret Cancer Centre, and Professor at the Dalla Lana School of Public Health at the University of Toronto, is co-senior author of the study.
Dr. Mamatha Bhat, Clinician-Scientist and Hepatologist at Ajmera Transplant Centre, Scientist at the Toronto General Hospital Research Institute (TGHRI), and Associate Professor in the Department of Medicine at the University of Toronto, is co-senior author of the study.
This work was supported by grants to UHN investigators from the Canadian Society of Transplantation, the American Society of Transplantation (AST), the Canadian Institutes of Health Research (CIHR), and UHN Foundation.
Sharma D, Gotlieb N, Chahal D, Ahn JC, Engel B, Taubert R, Tan E, Yun LK, Naimimohasses S, Ray A, Han Y, Gehlaut S, Shojaee M, Sivanendran S, Naghibzadeh M, Azhie A, Keshavarzi S, Duan K, Lilly L, Selzner N, Tsien C, Jaeckel E, Xu W, Bhat M. GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients. Nat Commun. 2025 May 28;16(1):4943. doi: 10.1038/s41467-025-59610-8. PMID: 40436838.
The UHN community mourns the loss of Dr. James Till, whose impactful research demonstrated the existence of stem cells, fundamentally transforming the future of medical science.
Born and raised on a farm in Lloydminster, Alberta, Dr. Till pursued his passion for science at the University of Saskatchewan, earning a B.Sc. in 1952 and an M.Sc. in physics in 1954. He then obtained a Ph.D. in biophysics from Yale University in 1957. Shortly thereafter, he was recruited to the Ontario Cancer Institute, now known as the Princess Margaret Cancer Centre (PM) at UHN.
At the Ontario Cancer Institute, Dr. Till collaborated with Dr. Ernest McCulloch, forming a partnership that would revolutionize biology. In 1961, through experiments involving the injection of bone marrow cells into irradiated experimental models, they provided the first description of blood-forming stem cells. Their 1963 publication in Nature further demonstrated that each spleen colony generated from these experiments originated from a single cell, offering the first functional definition of stem cells. Collaborating with Dr. Lou Siminovitch, they further showed that these marrow cells possessed the capacity for self-renewal.
Dr. Till continued to advance stem cell research for over 15 years, exploring the potential of stem cells to differentiate into various cell types and the feasibility of isolating viable stem cells.
The impact of this work has been profound, laying the groundwork for bone marrow transplants and forming the basis for numerous stem cell therapies and regenerative medicine approaches aimed at repairing or regenerating damaged tissues and organs.
In the 1980s, Dr. Till expanded his research interests to encompass various aspects of cancer care, including quality of life, research ethics, and the decision-making capacities of cancer patients. He also explored the role of the Internet as a source of information, support, and advocacy, and examined the impact of this information on patient care.
Dr. Till served as a Professor Emeritus at the University of Toronto. His numerous accolades include the Canada Gairdner International Award in 1969, appointment as an Officer of the Order of Canada in 1994, election as a Fellow of the Royal Society in 2000, and induction into the Canadian Medical Hall of Fame in 2004.
Dr. Till’s legacy endures through the transformative research he inspired and the many scientists he mentored, whose work continues to advance stem cell science and improve lives worldwide.
"Dr. James Till’s work was not only essential to the discovery and conception of stem cells but also set a new standard for scientific rigour and collaboration,” said Dr. Brad Wouters, Executive Vice President of Science and Research at UHN. “His contributions will continue to resonate through the generations of scientists he mentored and the countless patients who have benefited from his discoveries.”
For more on Dr. Till’s work and enduring legacy, please see the following videos from UHN that marked the 50th anniversary of the discovery that confirmed the existence of stem cells: Till and McCulloch tribute "Mentors"; Till and McCulloch tribute “Legacy”.
Welcome to the latest issue of Research Spotlight.
As Canada’s largest research hospital, UHN is a national and international source for discovery, education, and patient care. This newsletter highlights top research advancements from over 5,000 members of TeamUHN—a diverse group of trainees, staff, and principal investigators who conduct research at UHN.
Stories in this month’s issue:
● Better Care for Movement Disorders: UHN researchers highlight the potential of a non-invasive treatment option for deep-brain stimulation for movement disorders like Parkinson Disease.
● Improving the Safety of Ventilation: Early trial finds nerve stimulation may protect breathing muscles in ventilated patients.
● AI Chatbot Enhances Cancer Care: A new AI chatbot offers on-demand support to people living with advanced breast cancer.
● Rhythms in Rehabilitation: Researchers explore dance as a form of physical rehabilitation during stroke recovery.
Read these stories and more online here. To read previous issues, see the newsletter archive.
In a new article from Nature, researchers from UHN’s Toronto General Hospital Research Institute (TGHRI) shared their vision for how artificial intelligence (AI) could transform the way scientists explore the inner workings of cells.
As techniques like genomics and proteomics (large-scale studies of genes and proteins to understand biological systems) generate high volumes of biological data, researchers are looking for tools to help make sense of it all. Inspired by large language models like ChatGPT, scientists are now aiming to create ‘multimodal foundation models or MFMs'—AI models that can understand and process different types of information, like text, images, and numbers, all at the same time—for biological data. These models could be trained on many types of data, including DNA, RNA, protein, and the spatial organization of cells.
Dr. Bo Wang, Chief AI Scientist at UHN and Senior Scientist at TGHRI, and his colleagues believe that by integrating large biological datasets, MFMs could give researchers the ability to predict cell types, identify gene functions, and understand gene regulation across different tissues and disease states. By breaking down DNA, RNA, and protein data into small chunks, similar to how large language models process words and phrases, AI systems can learn to understand both the fine details, such as individual genes, and the bigger picture, such as how whole systems interact.
However, creating these powerful models requires significant amounts of high-quality data, as well as advanced computing power. Although the technology holds tremendous promise, challenges remain, including risks like “hallucination”, where AI produces incorrect but plausible results.
Despite these hurdles, Dr. Wang and his colleagues aim to build flexible models that can perform multiple tasks, ranging from simulating gene activity to predicting cell behaviour under specific conditions, such as genetic changes or drug treatments. The researchers believe that this approach could herald a new era in biomedical research, where AI plays a crucial role in decoding the complexities of life.
Dr. Haotian Cui, former doctoral student in Dr. Bo Wang’s lab, is the first author of the study.
Dr. Bo Wang, a Senior Scientist at Toronto General Hospital Research Institute and Assistant Professor in the Departments of Computer Science and Laboratory Medicine & Pathobiology at the University of Toronto, is the co-senior author of the study.
Dr. Fabian J. Theis, the Head of the Computational Health Center, Director of the Institute for Computational Biology at Helmholtz Munich, is the co-senior author of the study.
This work was supported by UHN Foundation.
Dr. Fabian J Theis consults for Immunai, CytoReason, Cellarity, BioTuring and Genbio AI, and has an ownership interest in Dermagnostix GmbH and Cellarity. Dr. Bo Wang serves as a scientific advisor to Shift Bioscience, Deep Genomics and Vevo Therapeutics, and acts as a consultant for Arsenal Bioscience.
See the manuscript for additional competing interests.
Cui H, Tejada-Lapuerta A, Brbić M, Saez-Rodriguez J, Cristea S, Goodarzi H, Lotfollahi M, Theis FJ, Wang B. Towards multimodal foundation models in molecular cell biology. Nature. 2025 Apr;640(8059):623-633. doi: 10.1038/s41586-025-08710-y. Epub 2025 Apr 16. PMID: 40240854.
Sleep apnea causes repeated interruptions in breathing during sleep, leading to fatigue, heart problems, and other serious health risks. Current diagnostic methods, like overnight sleep studies, are costly, intrusive, and difficult to access. Researchers from the KITE Research Institute have applied artificial intelligence (AI) to analyze breathing sounds, offering a less invasive and more affordable method of sleep apnea diagnosis.
There are two main types of sleep apnea—obstructive and central—and distinguishing between them is essential, as each has different causes and treatments. Obstructive sleep apnea is caused by a blocked airway, while central sleep apnea occurs when the brain does not signal the muscles that control breathing. Misdiagnosis can be harmful, as treatments for obstructive sleep apnea, like continuous positive airway pressure (CPAP), may worsen outcomes for people with central sleep apnea.
Recently, breathing sounds have gained attention as a method for sleep apnea diagnosis, as they can be captured using a small, wearable microphone. To further explore this non-invasive and portable diagnostic method, Dr. Azadeh Yadollahi, a Senior Scientist at the KITE Research Institute, trained AI models to recognize differences in breathing sounds recorded during sleep.
The research team recruited 50 participants for overnight sleep studies, measuring brain activity, heart rhythm, breathing effort, and breathing sounds throughout the night. The measurements were then used to train six AI models to distinguish between obstructive and central sleep apnea events. By analyzing differences in the frequency and intensity of breathing sounds, the AI models differentiated sleep apnea events with more than 80% accuracy.
The findings of this study confirmed that breathing sounds can accurately differentiate obstructive and central sleep apnea, enabling the development of compact, at-home diagnostic devices. Further developments of this method could support earlier, safer, and more precise diagnoses for people with sleep apnea.
Dr. Shumit Saha, the first author of the study, is a former PhD Student in the lab of Dr. Azadeh Yadollahi.
Dr. Azadeh Yadollahi, the lead author of the study, is a Senior Scientist at the KITE Research Institute and a Canada Research Chair in Cardiorespiratory Engineering, Tier 2. At the University of Toronto, Dr. Yadollahi is an Associate Professor at the Institute of Biomedical Engineering and an Association Member of the Department of Electrical and Computer Engineering.
This work was supported by UHN Foundation.
Saha S, Ghahjaverestan NM, Yadollahi A. Separating obstructive and central respiratory events during sleep using breathing sounds: Utilizing transfer learning on deep convolutional networks. Sleep Med. 2025 Mar 29. doi: 10.1016/j.sleep.2025.106485.
On Saturday, May 10, 2025, UHN STEM Pathways, together with Sinai Health’s SciHigh, hosted an event for Science Rendezvous—a free, family-friendly science festival featuring hands-on STEM (science, technology, engineering, and mathematics) activities for youth.
The event, held at Hennick Bridgepoint Hospital, welcomed over 200 youth and their families for an afternoon filled with engaging demonstrations from each of UHN’s six research institutes:
● KITE Research Institute: Children used candy to construct models of the human spine and to simulate how the brain sends signals to muscles using a robotic device.
● Princess Margaret Cancer Centre: Participants made crafts that focused on cancer awareness and explored tumour models.
● Krembil Research Institute: Activities featured paper brain hats, and models of the bones of the hand using pipe cleaners.
● Toronto General Hospital Research Institute: Participants used microscopes to learn about different experimental models and created paper lung models.
● McEwen Stem Cell Institute: Children made bracelets for diabetes awareness and learned about stem cells through puzzles.
● The Institute for Education Research (TIER): Volunteers led a computer-based activity showing how researchers use technology to study human behaviour.
This event blended fun and education for participants of all ages and was made possible by the hard work of 21 volunteers from across UHN. Through Science Rendezvous, UHN STEM Pathways emphasizes the value of making science more accessible.
UHN STEM Pathways is a Toronto-based outreach program that aims to inspire and educate students from kindergarten to grade 12 through interactive tours, scientist panels, hands-on workshops, and classroom visits. For more information on their current offerings, click here or contact [email protected].
Research conducted at UHN's research institutes spans the full spectrum of diseases and disciplines, including cancer, cardiovascular sciences, transplantation, neural and sensory sciences, musculoskeletal health, rehabilitation sciences, and community and population health.
Learn more about our institutes by clicking below:
