ReviewSaudi medical journal2026
Artificial Intelligence in Pharmaceutical Care:
Review in Saudi medical 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into pharmaceutical care represents a paradigm shift in healthcare delivery, offering unprecedented opportunities to revolutionize medication management, accelerate drug development, and enhance patient outcomes. This comprehensive review synthesized evidence from global literature (2021-2025) examining AI applications across drug discovery and development, clinical trials optimization, medication therapy management, and pharmacy operations. The AI demonstrated prediction accuracies of 86-98% in drug discovery, 80% improvement in clinical trial recruitment efficiency, and 32.7% increase in medication adherence over standard care. The global AI drug discovery market, valued at $1.5 billion (2023), is projected to reach $11.8 billion by 2030, reflecting substantial industry investment. However, implementation challenges persist including data quality concerns (30% accuracy in some datasets), regulatory compliance issues, and algorithmic bias (8-12% performance variations across demographics). Successful implementation requires coordinated efforts across technological development, regulatory frameworks, healthcare professional training, and continuous validation protocols addressing technical, organizational, and ethical dimensions simultaneously.
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.