Utilisation de l’intelligence artificielle en pharmacie : une revue narrative
Bibliographic record
Abstract
RÉSUMÉ Contexte : L’intelligence artificielle (IA) est une avancée technologique qui consiste à amener une machine à imiter une forme d’intelligence. Objectifs : L’objectif principal est d’effectuer une revue narrative des études évaluant la faisabilité et l’impact de l’IA en pharmacie. L’objectif secondaire est de développer une carte heuristique entourant l’IA en santé. Sources des données : Nous avons consulté quatre bases de données, soit PubMed, Medline, Embase et CINAHL. Sélection des études et extraction des données : Quatre stratégies de recherche ont été élaborées. Sélection des articles sur la base du titre, de l’abrégé puis du texte par une assistante de recherche, suivie d’une révision par un pharmacien de l’équipe. Les articles pris en compte doivent décrire ou évaluer la faisabilité ou l’impact de l’IA en pharmacie. Synthèse des données : À partir de la revue documentaire, 362 articles ont été sélectionnés au départ, 18 d’entre eux ont été retenus selon les critères d’inclusion. De façon générale, on note que les études ont été surtout menées aux États-Unis (72 %, 13/18). Les études portent, par ordre d’importance décroissant, sur la prédiction de la réponse aux traitements et la prédiction d’effets indésirables (33 %, 6/18), la priorisation des patients (28 %, 5/18), l’adhésion thérapeutique (22 %, 4/18), la validation d’ordonnances et la prescription électronique (17 %, 3/18) et d’autres thèmes (p. ex. diagnostic, coûts, assurance, vérification de volumes de seringue). Conclusions : Cette revue narrative met en évidence 18 études évaluant la faisabilité et l’impact de l’IA en pharmacie. Ces études ont utilisé différentes approches méthodologiques dans divers domaines d’application, en officine comme en établissement de santé. Il est encore trop tôt pour prédire les retombées de l’IA en pharmacie, mais ces études soulignent l’importance de s’y intéresser. ABSTRACT Background: Artificial intelligence (AI) can be described as an advanced technology in which machines display a certain form of intelligence. Objectives: The primary objective was to perform a narrative review of studies evaluating the feasibility and impact of AI in pharmacy. The secondary objective was to create a mind map of AI in health care. Data Sources: Four databases were consulted: PubMed, Medline, Embase, and CINAHL. Study Selection and Data Extraction: Four search strategies were developed. Initial selection of articles was based on their titles and abstracts; the full texts were then evaluated by a research assistant, with review by a pharmacist. Articles were included if they described or evaluated the feasibility or impact of AI in pharmacy. Data Synthesis: A total of 362 articles were identified by the literature review, of which 18 met the inclusion criteria. The studies were mainly conducted in the United States (72%, 13/18). The article topics were, in decreasing order, prediction of response to treatments and adverse effects (33%, 6/18), patient prioritization (28%, 5/18), treatment adherence (22%, 4/18), validation of prescriptions and electronic prescription (17%, 3/18), and other themes (e.g., diagnosis, costs, insurance, and verification of syringe volume). Conclusions: This narrative review highlighted 18 studies evaluating the feasibility and impact of AI in pharmacy. The studies used various methodologies in different settings, both retail pharmacies and hospital pharmacies. It is still too soon to predict the implications of AI for pharmacy, but these studies emphasize the importance of attention in this area.
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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".