Promesses de l’IA en santé - Fiche 2
Bibliographic record
Abstract
Cette fiche présente les principales promesses (ou les bénéfices potentiels ou cas d’usage potentiels) de l’IA en santé du point de vue du système de santé universel, celui qui vise à améliorer la santé de la population ainsi que l’expérience des patients et des soignants (Aung, Wong, and Ting 2021; Lovis 2019). Ces bénéfices potentiels sont présentés pour les usages les plus prometteurs des systèmes d’intelligence artificielle (SIA), en mettant l’accent sur les développements récents des techniques d’IA (p. ex. apprentissage automatique). Cette fiche ne couvre pas l’ensemble du potentiel des SIA qui pourraient améliorer la santé, notamment pour soutenir la recherche, la découverte, et la santé publique (p. ex. prévention et protection). Il s'agit de la deuxième fiche d'une série de 4 développée dans le cadre d'un mandat réalisé pour le Ministère de la Santé et des Services sociaux du Québec (MSSS).
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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".