Bioethical challenges and artificial intelligence, focus Quebec/France
Bibliographic record
Abstract
The authors discuss the potential benefits of AI for the healthcare system. To do this, they consider the importance of ensuring the confidentiality of medical data, maintaining a patient-doctor relationship imbued with humanity, as well as liability remedies specific to stemming the potential abuses of AI. In the healthcare sector, both in France and in Canada, AI is expected to be a tool for transforming and democratising healthcare by improving its quality, safety and effectiveness. We therefore need to analyse the legal framework in place to ensure that these objectives are met. The question is addressed on the basis of a study of the rules through the diversity of their conception, their implementation by the courts (case law) and their analysis by the authors making up the doctrine. The authors discuss the potential benefits of AI for the healthcare system. To do this, they consider the importance of ensuring the confidentiality of medical data, maintaining a patient-doctor relationship imbued with humanity, as well as liability remedies specific to stemming the potential abuses of AI. While the regulations applicable to AI are considered to be relevant and appropriate for taking account of the various issues, particularly in terms of privacy and liability, it will be necessary to remain attentive to their implementation in order to ensure that the objectives are effectively achieved.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".