Utilisation de l'intelligence artificielle en pharmacie : une revue narrative

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.