ReviewFuture healthcare journal2026
Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.
Review in Future healthcare journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Artificial intelligence (AI) is increasingly being integrated into healthcare systems, offering new opportunities to enhance the safety, efficiency and effectiveness of clinical pharmacy services. This scoping review aimed to systematically map the current applications of AI in clinical pharmacy practice and to identify the medication-management functions supported by these technologies. Methods: A literature search was conducted following PRISMA-ScR guidelines, covering studies published between 2010 and 2025 in databases such as PubMed, Web of Science and Scopus. Included studies focused on the application of AI in clinical pharmacy services within hospital and community settings. Studies were screened at the title, abstract and full-text levels, followed by data extraction and synthesis. Eligible studies were required to be peer-reviewed, written in English, and to include relevant keywords exploring the intersection of artificial intelligence and pharmacy practice. Results: A total of 36 publications were included in the final assessment and analysis. Most studies focused on AI applications for the detection of drug interactions and adverse drug effects (n = 12, 33.3%), followed by studies for medication errors and potentially inappropriate medications (n = 11, 30.6%). Machine learning techniques, including natural language processing and deep learning, were the most commonly used AI technologies. Conclusions: The development of AI-powered applications and tools for clinical pharmacy services is a promising approach. However, significant efforts, in collaboration with relevant stakeholders, are needed to explore how these AI technologies can add value to clinical pharmacy services in both community and hospital settings.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.