Dr. Farzad Khalvati, PhD, is Director of Radiology AI, Senior Scientist, and Endowed Chair in Medical Imaging and Artificial Intelligence at The Hospital for Sick Children (SickKids) and University of Toronto. He is Associate Professor in the Department of Medical Imaging and Institute of Medical Science, with cross appointments to the Department of Computer Science and Department of Mechanical and Industrial Engineering at the University of Toronto. He is also a Faculty Affiliate at Vector Institute.

Dr. Farzad Khalvati, Principal Investigator

Dr. Khalvati provides strategic leadership to advance a high-impact, collaborative, and clinically grounded research enterprise within the Department of Radiology. His focus is on accelerating innovation in precision medicine including precision radiology, precision oncology, and precision neurology through multimodal AI, while aligning research priorities with departmental, institutional, and healthcare missions. He fosters a culture of rigor, inclusivity, and mentorship that empowers faculty and trainees to pursue transformative, patient-centered research.

Dr. Khalvati has a proven record of building and sustaining multidisciplinary research programs, securing more than $7M in competitive funding, and enabling clinically driven partnerships across medical subspecialties, engineering, industry, and healthcare organizations. As a prolific investigator with over 130 peer-reviewed publications and 70 scientific abstracts, he leads by example while supporting faculty success through mentorship, grant development, and strategic resource allocation.

A core priority of Dr. Khalvati’s leadership is faculty development and team science. He has mentored and managed diverse teams of more than 85 trainees, staff, and junior faculty transitioning to research independence. He has established and continuously led two core courses in AI in medicine, AI for Medical Imaging and Natural Language Processing for Medicine, for the past five years, providing a sustainable educational pipeline that equips faculty, fellows, and trainees with the skills needed to lead and participate in AI-driven clinical research.

With specialized expertise in clinical validation, regulatory strategy, and translational pathways for medical devices and AI technologies, Dr. Khalvati is committed to bridging discovery and clinical deployment. His leadership vision is to position the department as a national and international leader in AI-enabled radiology research, driving innovation that improves patient outcomes, advances precision medicine, and promotes equitable, patient-centered care.

EXPERTISE
• Precision Medicine and Precision Child Health, leveraging advanced AI techniques to deliver personalized diagnostics and treatment strategies tailored to individual patients, including pediatric populations.
• Development of optimized and multimodal AI solutions that integrate diverse data sources such as medical imaging, pathomics, genomics, and health informatics to enhance healthcare outcomes.
• Pioneering efforts in Explainable AI (XAI) to ensure transparency, trust, and interpretability in AI models, empowering clinicians to make informed decisions.
• Focused on designing AI for equitable healthcare, addressing biases to ensure fair access and outcomes for diverse populations.
• Emphasis on clinician-centered AI tools that seamlessly integrate into clinical workflows, enhancing usability and reducing barriers to adoption.
• Dedicated to the clinical translation of AI models, bridging the gap between research and real-world implementation, and ensuring robust validation, regulatory compliance, and impactful deployment in healthcare settings.

Dr. Khalvati received his PhD in Electrical and Computer Engineering from University of Waterloo and before joining University of Toronto, he worked as an imaging and AI scientist in biomedical industry and then as a Postdoctoral Research Associate at Sunnybrook Research Institute. Dr. Khalvati is a recipient of several awards from NSERC, OCE, and CIHR and his dissertation was selected and patented by the University of Waterloo Commercialization Office.