Artificial Intelligence Alongside Physicians in Canada: Reality and Risks
Bibliographic record
Abstract
Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the healthcare system with their immense potential to diagnose, personalize treatments, and reduce physician burnout. These technologies are highly dependent on large datasets to learn from and require data sharing across organizations for reliable and efficient predictive analysis. However, adoption of AI/ML technologies will require policy imperatives to address the challenges of data privacy, accountability, and bias. To form a regulatory framework, we propose that algorithms should be interpretable and that companies that utilize a black box model for their algorithms be held accountable for the output of their ML systems. To aid in increasing accountability and reducing bias, physicians can be educated about the inherent bias that can be generated from the ML system. We further discuss the potential benefits and disadvantages of existing privacy standards ((Personal Information Protection and Electronic Documents Act) PIPEDA and (Personal Information Protection and Electronic Documents Act) GDPR) at the federal, provincial and territorial levels. We emphasize responsible implementation of AI by ethics, skill-building, and minimizing data privacy breaches while boosting innovation and increased accessibility and interoperability across provinces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".