Role of artificial intelligence in pharmacy practice: A narrative review

药店 梅德林 医学 观察研究 鉴定(生物学) 患者安全 精密医学 医疗保健 家庭医学 病理 政治学 经济增长 植物 生物 经济 法学
作者
Adrian Wong,Erin Wentz,Nicholas Palisano,Manar Dirani,Pansy Elsamadisi,Farah Qashou,Leo Anthony Celi,Omar Badawi,Lama Nazer
出处
期刊:JACCP: journal of the American College of Clinical Pharmacy [Wiley]
卷期号:6 (11): 1237-1250 被引量:10
标识
DOI:10.1002/jac5.1856
摘要

Abstract Artificial intelligence (AI) has emerged as a potentially useful tool in transforming healthcare. Roles for AI include drug safety, operations, as well as precision medicine to improve patient outcomes. Within pharmacy practice, limited guidance is available describing the purpose of AI. This gap is important to address given the current growth of AI in medicine. The purpose of this narrative review was to review the literature evaluating the potential role of AI in three relevant areas of pharmacy practice. A review of Medline and Embase from January 2004 to June 2022 evaluating search terms involving AI and drug safety, pharmacy operations, or precision medicine. Articles had to be focused on human subjects, be either an observational cohort study or randomized control trial, and involve medication use to be included in this review. Data collection included the study design, AI methodology, and primary outcome and associated AI model performance measures. A total of 24 232 articles were identified from the search strategy, with 403 articles meeting inclusion criteria (1.7%). A total of 22 articles were included in this review. Articles focused on drug safety included safe use of opioids and the role of social media in identifying adverse drug reactions. In terms of pharmacy operations, articles included the identification of medications at risk for shortage and visual identification of medications. Finally, for precision medicine, articles included determining likelihood of beneficial effects of medications for new diagnoses and dosing of warfarin. The role of AI in improving patient outcomes is numerous. However, challenges that remain include transparency of generated AI models, generalizability, and lack of external validation. Future areas of focus include education of pharmacy clinicians on the role of AI and the increased use of open‐source models/data to allow for external validation of AI.

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