ReviewDigital health
Artificial intelligence in clinical pharmacy-A systematic review of current scenario and future perspectives.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Prediction tools to prioritise hospitalised adult patients at risk of drug related problems: An umbrella review.PLOS digital health · 2026Article
- Descriptive analysis of prescription interception patterns: characterizing medication safety risks in an outpatient setting.Frontiers in pharmacology · 2026Article
- Digital health in clinical pharmacy practice: transforming precision medicine and pharmacometrics in Africa.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Medication prescription errors represent a significant and persistent challenge within healthcare systems globally, constituting a primary focus for clinical pharmacy practice. Additional complexities involve the optimization of drug dosing and the implementation of personalized medicine. This review aims to synthesize the current advancements in artificial intelligence (AI) applications within clinical pharmacy and to discuss future directions for the field. Methods: To present this narration, 30 articles were reviewed in total. The literature search was done using electronic databases, for example, PubMed, Medline, and Google Scholar, with the help of some keywords. Only articles published in peer-reviewed journals were included. Results: A total of 30 articles that demonstrated the utility of AI-based applications in clinical pharmacy were included for further analysis. Across all included studies, AI was utilized primarily for the detection of adverse drug events, clinical decision support, verification of prescription accuracy, and pharmacometrics. Secondary applications included providing recommendations to pharmacists for medication therapy management and, importantly, predicting the therapeutic response to a given treatment in conjunction with its cost-effectiveness. Conclusion: Artificial intelligence-based algorithms have been identified as applicable tools for the early detection of adverse drug events and prescription errors, the prediction of individual drug response, and the design of patient-specific treatment plans. Prior to broad clinical implementation, future multicenter, prospective studies employing standardized clinical endpoints, external validation, and cost-effectiveness analyses are required.
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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.