La délégation des actes médicaux dans un contexte de médecine moderne, notamment par l’utilisation de l’intelligence artificielle : enjeux en responsabilité civile québécoise
Bibliographic record
Abstract
This essay explores a form of transformation in modern medicine related to the delegation of medical acts by doctors. There are two forms of delegation: human (to physician assistants such as nurses, residents, etc.) and technological (delegation of tasks to artificial intelligence). In Quebec, human delegation is governed by laws and regulations specifying permitted acts and categories of authorized professionals. This practice raises legal issues concerning the civil liability of the delegating physician, the delegate and the healthcare establishment. Artificial intelligence technologies are increasingly present in healthcare, particularly as diagnostic, treatment and management tools. This technology poses challenges in terms of reliability, understanding how it works (“black box” effect), data protection and informed patient consent. Quebec law has not yet adapted to this rapidly evolving reality. This essay therefore reflects upon the legal issues related to civil liability, legal consent and ethical considerations resulting from this profound transformation of the healthcare system.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".