Innovative Machine Learning for Precision, Analytics, Care & Treatment
Led by Dr. Divya Sharma, York University
Innovative Machine Learning for Precision, Analytics, Care & Treatment
The IMPACT-AI Lab at York University, led by Dr. Divya Sharma, is dedicated to building intelligent, ethically grounded, and clinically impactful AI systems for biomedical research and healthcare. Our work bridges advanced machine learning methods, biological data science, and real-world medical decision-making. We collaborate closely with clinicians and researchers at the University Health Network (UHN), including Toronto General Hospital, Princess Margaret Cancer Centre, and the Schroeder Arthritis Institute. Our research spans diverse domains such as liver transplantation, osteoarthritis, mental health, and precision oncology. From predicting graft injury in liver transplant recipients to identifying multi-omic signatures of osteoarthritis progression and designing generative AI companions for mental health support, we harness AI to enable personalized, explainable, and equitable care. Our interdisciplinary team develops and applies deep learning, reinforcement learning, and generative AI methods across structured, unstructured, and multi-modal biomedical data. We are committed to building AI systems that not only perform but also integrate into clinical workflows, ensuring relevance, transparency, and real-world utility.
Oct, 2025
PI Dr. Divya Sharma is proud to recieve the Petro Canada Emerging Innovator Award for her breakthrough research in equitable GenAI and Agentic AI frameworks
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July, 2025
Secured federal funding to advance generative AI in cancer research
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May 1, 2025
The IMPACT-AI Lab is proud to be part of two newly funded CIHR Grants, A team and a project grant where as co-PI, Dr. Divya Sharma leads the development of AI models to predict long-term graft health integrating clinical data with social determinants
More Details<Our key research areas
RL applied in the area of precision medicine to develop personalized treatment strategies and optimize clinical decision-making processes.
Large language models for mental health support and therapeutic interventions, agentic AI systems for liver disease management and clinical decision support, and generative models for genomic data synthesis and interpretation.
Multimodal data based deep learning models integrating multiple domains and applied to Mental Health Data and Osteoarthritis Data.