IVADO is supporting this research program with $1.2M as part of our Strategic Research Funding Program launched in 2021. We invite you to visit the program’s main page to view its objectives and better understand the process that supported this project.

Program description

Artificial intelligence (AI) technologies hold the potential to transform healthcare. These technologies are emergent in logistics and imaging, and hundreds of algorithms are now being developed to help support care delivery. Many challenges remain, however, when it comes to scale-up for use in the field. One such challenge is ensuring the generalizability of such algorithms. How can we guarantee the effectiveness of one model on a data set with characteristics that differ from the one the algorithm learned with? For example, an algorithm trained using data from a specific population may not perform as well when applied to a different population.

This program therefore aims to study new methods for improving generalization, and pursues four objectives. First, set up a research environment enabling the study of methods likely to improve generalization in real-world contexts. Second, optimize data flows obtained in real-world healthcare settings to serve algorithm research. Next, investigate specific issues related to algorithm generalization and secondary use of medical data. Lastly, create an open data set that can be used to build upon the research program findings.

Principal Investigators

Michaël Chassé

Associate Professor

Université de Montréal, CRCHUM

Nadia Lahrichi

Full professor

Polytechnique Montréal, CIRRELT

Dr An Tang

Full Professor

Université de Montréal, CRCHUM