ReviewJournal of research in pharmacy practice
Impact of Artificial Intelligence on the Future of Clinical Pharmacy and Hospital Settings.
Review in Journal of research in pharmacy practice. 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review aimed to comprehensively evaluate the impact of artificial intelligence (AI) on the future of clinical pharmacy practice and hospital settings. To achieve this, a thorough literature search was conducted across major databases, including PubMed, Scopus, Web of Science, Embase, and Google Scholar, covering studies published up to July 2025. Relevant keywords - such as "artificial intelligence," "machine learning," "clinical pharmacy," "hospital pharmacy," "medication management," and "decision support systems" - were used to identify studies assessing the applications, challenges, and outcomes of AI in these contexts. All eligible articles were narratively analyzed. The findings indicated that AI has a significant role in optimizing pharmaceutical inventory management, reducing medication errors, enhancing the accuracy and safety of drug dispensing, and streamlining drug distribution processes in hospital environments. Advancements in machine learning algorithms and clinical decision support systems not only assist pharmacists in selecting optimal therapeutic regimens and detecting drug-drug interactions but also facilitate the delivery of personalized patient care and prediction of medication needs. In addition, AI integration was shown to improve workflow efficiency, reduce administrative workload, and enhance both pharmacist training and job satisfaction. In conclusion, the integration of AI into clinical pharmacy and hospital settings emerges as a transformative factor, promising to elevate the quality of pharmaceutical care, improve patient outcomes, and shape a more dynamic and efficient future for the pharmacy profession.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.