Research Post
Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.
Feb 9th 2023
Research Post
Feb 6th 2023
Research Post
Read this research paper, co-authored by Fellow & Canada CIFAR AI Chair at Russ Greiner: Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms
Jul 7th 2022
Research Post
Read this research paper, co-authored by Fellow & Canada CIFAR AI Chair Russ Greiner: Prediction of Obsessive-Compulsive Disorder: Importance of neurobiology-aided feature design and cross-diagnosis transfer learning
Looking to build AI capacity? Need a speaker at your event?