Grant: Canada Research Chairs

Digital technology is transforming healthcare, worldwide. It is altering workflows for clinicians and hospitals by providing unique opportunities to revolutionize clinical decision making during medical interventions. Decision making based on digital technology and machine learning is only reasonable with abundant, high-quality data. With the heterogeneity of disease and patients, and the availability of low-cost devices, noisy data across a broad range of patients and diseases are readily available. Labels, required to build machine learning methods using this data, are annotations that represent the presence, location, and/or extent of a disease, and are typically determined by expert clinicians. While millions of data points can now be stored from images and signals during interventions, labels are subjective and extremely sparse. Hence, they lack the high-fidelity, one-to-one correspondence required to build accurate machine learning solutions. Innovative methods and machine learning models are needed to address these challenges and augment decision making in medical interventions. In addition to being accurate, these models should be interpretable and actionable to improve the responses of clinicians. As a Tier 1 CRC, Dr. Mousavi will undertake an innovative program of research to develop robust and actionable machine learning methods and translate them to impactful solutions for medical interventions. The overarching objective is to fundamentally change disease diagnosis, interventions, and identification of optimized and tailored treatments for individual patients, disrupting how clinical information is thought of and transforming clinical decision making. To achieve these goals the research program bridges broad multi-disciplinary domains to translate computing science and engineering inventions from idea through bench to bedside, altogether conceiving and realizing the next generation of medical interventions. The program also focuses on training the next generation of computer scientists and engineers with specialized experience in medical interventions, while engaging diverse groups of trainees including women, Black and Indigenous communities, and internationally educated professionals. It incorporates partnerships with industry and health institutions and contributes to the critical dialogue surrounding societal impacts of AI through health and wellbeing for all.