Bibliographic record
Abstract
Artificial intelligence (AI) tools are portended to dramatically improve the quality and safety of health care as well as efficiency. AI tools will be used to track patient data, triage care, read medical images, diagnose disease, make treatment decisions, support patients in health promotion, and deliver primary, acute, mental, and long-term care. AI innovations are predicted to not only assist but potentially substitute human caregivers, medical service providers, diagnosticians, and expert decision makers. Yet Canada’s complex ecosystem of governance for health care across multiple levels of government, including direct regulation, self-regulation and tort law/delicts, has not been assessed for its ability to bolster “good” AI innovation and deter “bad” AI-related activities across the health care system. Consequently, pathways to the adoption of promising AI technologies may be hampered by uncertainty as to what current laws require to, for example, comply with health privacy laws and avoid tort liability. Moreover, a number of leading task forces and various scholars have opined that legal and governance structures must be reformed to address concerns such as algorithmic bias. For AI technologies to be adopted into and improve health care, it is important to have both regulatory clarity and evidence on whether and how present laws need to be reformed. Canada faces significant challenges in establishing a clear and coordinated regulatory environment for promising AI technologies given its patchwork of patient safety and privacy laws across provinces and territories and its historical reliance on self-regulation of health care professionals.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".