A 91亚色 researcher will co-lead two major national research projects to advance health equity and innovation in liver transplant care by using artificial intelligence (AI).
Divya Sharma, assistant professor in the Faculty of Science who leads the , is a co-principal investigator on two Canadian Institutes of Health Research (CIHR) grants: a Team Grant of nearly $2 million and a Project Grant of close to $1 million.聽
The Team Grant project, titled 鈥,鈥 brings together experts from across disciplines to address systemic gaps in liver transplant care.
The interdisciplinary team includes clinicians, researchers and policy experts from institutions including University Health Network, the University of Toronto and other leading transplant centres.
The team aims to build a national framework that uses AI to identify and address disparities in access to liver transplants and in post-transplant outcomes. Sharma is leading the development of a predictive model that integrates both clinical data and social determinants of health 鈥 such as income, geography and structural barriers 鈥 to support long-term graft health and inform more equitable care.
鈥淏y incorporating social and structural factors into our modelling, we aim to create a more holistic and equitable approach to liver transplant care,鈥 says Sharma.
Sharma is also a co-principal investigator on a second CIHR-funded project, 鈥,鈥 which received $933,300. This five-year study, led in partnership with researchers at the University Health Network, focuses on predicting and preventing graft fibrosis 鈥 a serious complication that affects up to 25 per cent of liver transplant recipients.
The team is developing a multimodal AI tool that combines clinical, pathology and imaging data to identify patients at high risk of graft scarring. The tool will be integrated into hospital electronic health records to support real-time decision making in clinical settings.
These projects build on Sharma鈥檚 recent study published in , where she and collaborators introduced GraftIQ, an AI tool that helps doctors diagnose liver graft problems without needing a biopsy. The model offers a safer and more accurate way to support care for liver transplant patients.
鈥淭his work has the potential to inform national policy and improve outcomes for patients who have historically faced barriers to access,鈥 says Sharma.
Liver transplantation is a life-saving procedure, but access to it 鈥 and the quality of care received after surgery 鈥 can vary significantly depending on a patient鈥檚 background and circumstances, she says. These projects are designed to close these gaps by building a data-driven framework that can be used by clinicians, health systems and policymakers to make more equitable decisions.
鈥淭he goal is to ensure that predictive tools are not only medically accurate but also representative of Canada鈥檚 diverse transplant population,鈥 says Sharma.