Canadian Medical Imaging Inventory 2022–2023: Provincial and Territorial Overview
Bibliographic record
Abstract
The average age of imaging equipment in Canada has increased for all imaging modalities over the past 2 decades. Most imaging equipment is more than 5 years old, and at least one-third of equipment is more than 10 years old. Investing in new equipment to meet growing demand may not increase imaging capacity without also considering staffing. There is no current international benchmark for the optimal number of imaging units per population, but there is a general assumption that too few units may limit access and increase wait times while too many units may encourage low-value imaging. Canada remains below the average for Organisation for Economic Co-operation and Development (OECD) countries and is positioned in the bottom 30% of OECD countries in units per million population for CT, MRI, and PET-CT. Canada is positioned in the bottom 50% of OECD countries for volume of publicly funded CT, MRI, and PET-CT exams per 1,000 population. Since 2012, both the overall numbers of MRI, PET-CT, and SPECT-CT units and the numbers of MRI, PET-CT, and SPECT-CT units per million people have grown. The number of units for all modalities operating in Canada has increased since 2019–2020, except for SPECT units. In most provinces, the number of SPECT units per population decreased, indicating that population growth outpaced installation. The imaging workforce is under strain. Clearing the backlog of exams deferred during the pandemic has exacerbated existing staffing shortages. New investment in radiology staffing, particularly imaging technologists, is required, including improved recruitment and retention policies. Compared to the prepandemic period, there are now fewer full-time radiology professionals in practice across Canada, with medical physicists experiencing the largest decline per million population. Wait times for medical imaging remain above the recommended maximum wait time in many jurisdictions and are influenced by a variety of factors. The adoption of supportive tools and technologies — such as clinical decision support tools, automated order entry, and AI-driven solutions — can assist the workforce, add value to imaging services, and increase access to medical imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".