Applications of artificial intelligence in current pharmacy practice: A scoping review.

BACKGROUND

Artificial intelligence (AI), a branch of computer science, has been of growing research interest since its introduction to healthcare disciplines in the 1970s. Research has demonstrated that the application of such technologies has allowed for greater task accuracy and efficiency in medical disciplines such as diagnostics, treatment protocols and clinical decision-making. Application in pharmacy practice is reportedly narrower in scope; with greater emphasis placed on stock management and day-to-day function optimisation than enhancing patient outcomes. Despite this, new studies are underway to explore how AI technologies may be utilised in areas such as pharmacist interventions, medication adherence, and personalised medicine. Objective/s: The aim of this study was to identify current use of AI in measuring performance outcomes in pharmacy practice.

METHODS

A scoping review was conducted in accordance with PRISMA Extension for Scoping Reviews (PRISMA-ScR). A comprehensive literature search was conducted in MEDLINE, Embase, IPA (International Pharmaceutical Abstracts), and Web of Science databases for articles published between January 1, 2018 to September 11, 2023, relevant to the aim. The final search strategy included the following terms: ("artificial intelligence") AND ("pharmacy" OR "pharmacist" OR "pharmaceutical service" OR "pharmacy service"). Reference lists of identified review articles were also screened.

RESULTS

The literature search identified 560 studies, of which seven met the inclusion criteria. These studies described the use of AI in pharmacy practice. All seven studies utilised models derived from machine learning AI techniques. AI identification of prescriptions requiring pharmacist intervention was the most frequent (n = 4), followed by screening services (n = 2), and patient-facing mobile applications (n = 1). These results indicated a workflow- and productivity-focused application of AI within current pharmacy practice, with minimal intention for direct patient health outcome improvement. Despite this, the review also revealed AI's potential in data collation and analytics to aid in pharmacist contribution towards the healthcare team and improvement of health outcomes.

CONCLUSIONS

This scoping review has identified, from the literature available, three main areas of focus, (1) identification and classification of atypical or inappropriate medication orders, (2) improving efficiency of mass screening services, and (3) improving adherence and quality use of medicines. It also identified gaps in AI's current utility within the profession and its potential for day-to-day practice, as our understanding of general AI techniques continues to advance.