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TRUVACE RECORD VERSION
record: TRV-2026-1794
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T06:54:40.564175Z
status: published
lens: p_space
sector: science
headline: Artificial intelligence in pharmacy and pharmaceutical sciences: Research output, trends, and thematic priorities - A descriptive analysis of web of science-indexed publications (2010-2025)
dek: Background: Artificial intelligence (AI) is rapidly transforming pharmacy and pharmaceutical sciences (PPS), yet the evolution, research priorities, and global landscape of AI-related research within this field have not been comprehensively characterized.
Methods: A descriptive bibliometric analysis of publications indexed in the Web of Science Core Collection was conducted for studies published between 2010 and November 9, 2025. Publication trends, document characteristics, journals, publishers, countries, auth…
gain_title: (none)
problem_title: Research on AI in pharmacy is heavily weighted toward computational drug design, leaving clinical decision support, pharmacovigilance, and manufacturing comparatively underrepresented.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Research on AI in pharmacy is heavily weighted toward computational drug design, leaving clinical decision support, pharmacovigilance, and manufacturing comparatively underrepresented.
problem_evidence: Drug Design and Modeling was the predominant thematic domain, encompassing 42.1% of publications | Pharmacovigilance and Medication Safety (1.2%) | remains predominantly focused on computational drug discovery and preclinical applications
quick_read: A bibliometric review of Web of Science records from 2010 to November 9, 2025 found 9671 publications on AI in pharmacy and pharmaceutical sciences. Output rose sharply, with 84.9% of the literature appearing from 2020 onward, led by China, India, and South Korea.

The imbalance matters because the patient-facing areas of pharmaceutical care receive little research attention, which may slow equitable translation of AI into practice. The study is descriptive, so it maps publication patterns and does not show whether AI tools improve care or outcomes.
limitation: The study is a descriptive bibliometric analysis of one database's publications and characterizes research output, not the real-world clinical effectiveness of AI in pharmacy.
tag: Evidence-backed problem
key_points: The analysis identified 9671 Web of Science publications on AI in pharmacy and pharmaceutical sciences from 2010 to November 9, 2025. | Publications from 2020 onward made up 84.9% of the literature. | Drug Design and Modeling accounted for 42.1% of publications, versus 3.6% for Clinical Pharmacology and Precision Therapeutics. | Pharmacy operations and decision support (1.6%), pharmacovigilance (1.2%), clinical trials (0.6%), and manufacturing and supply chain (0.5%) were underrepresented. | The most cited papers were methodological AI studies rather than pharmacy-specific applications.
rundown: The authors used descriptive statistics, keyword analysis, and network visualization to examine trends, journals, publishers, countries, authors, themes, citation impact, and funders.

Original research articles in English predominated. The Journal of Chemical Information and Modeling, Journal of Pharmaceutical Sciences, and Briefings in Bioinformatics were the leading venues.

The authors conclude that clinical implementation, patient-centered pharmacy practice, pharmacovigilance, and broader geographic participation need greater emphasis.
sources:
- peer_reviewed | Exploratory Research in Clinical and Social Pharmacy | https://doi.org/10.1016/j.rcsop.2026.100852 | 2026-09-21
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