Big Data & Artificial Intelligence
Big data and predictive analytics encompass large, complex datasets generated across the healthcare landscape—from electronic health records and connected devices to health apps and beyond. Exploiting these data in areas such as quality-indicator analysis, program evaluation, artificial intelligence, diagnostics, and biomarkers helps transform clinical practice, enhance patient outcomes, and drive medical innovation.
Platform Objectives
Artificial intelligence has transformed nearly every aspect of modern life, and healthcare is no exception. These advancements are only possible with access to large, high-quality datasets and the ability to analyse them responsibly and effectively.
The AI and Big Data Platform focuses on leveraging state-of-the-art AI methods—ranging from medical image analysis to predictive modelling of adverse health outcomes—to support evidence-based care for older adults.
With access to extensive clinical databases, longitudinal aging cohorts, and McGill University’s high-performance computing environments, our platform aims to accelerate the development, validation, and translation of AI solutions into real clinical settings. We emphasize data quality, reproducibility, fairness, and clinical relevance to ensure that every model we build or support can meaningfully improve decision-making, screening, and early detection in geriatric care.
Our goal is to bridge cutting-edge AI research with real-world clinical impact, helping clinicians, researchers, and innovators unlock the full potential of data-driven healthcare.
Transforming Health and Resilience for Healthy Lives (THRIVE)
THRIVE is a large multi-cohort research initiative focused on redefining and strengthening the concept of Intrinsic Capacity (IC) in older adults—an emerging framework used to understand healthy aging and predict functional decline.
By combining data from major international longitudinal aging studies with Canadian cohorts such as the CLSA, THRIVE uses advanced statistical methods and AI models to build, validate, and compare IC constructs across populations. The project also develops automated, AI-driven tools capable of predicting adverse health outcomes and scoring IC directly from clinical data. Ultimately, THRIVE provides a robust, data-driven foundation for improving early identification, prevention strategies, and care pathways in primary care and geriatric settings.
Projects
Platform Coordinator
For any questions regarding this platform, please contact Mahdi Imani, PhD.
Mahdi Imani, PhD
Mahdi Imani is a Biomedical Engineer and AI specialist whose work spans medical imaging, machine learning, device development, and data-driven health innovation. He holds a PhD in Medicine from the University of Melbourne, where he developed advanced analytical and AI-based approaches to better understand musculoskeletal and aging-related health. His expertise combines engineering, clinical research, and computational sciences to support the development of technologies that meaningfully improve patient care.
Mahdi currently serves as a Postdoctoral Fellow at McGill University and coordinates technology and AI initiatives at CEDurable. His work brings together clinicians, researchers, engineers, policy partners, and industry collaborators—across Canada and internationally—to evaluate, adapt, and integrate emerging technologies into real-world health systems. He is particularly focused on building practical pathways that help innovators move from early prototypes and datasets to safe, effective, and scalable solutions in everyday clinical practice.