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Artificial intelligence in pharmacy and pharmaceutical sciences: Research output, trends, and thematic priorities - A descriptive analysis of web of science-indexed publications (2010-2025)

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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1794· cite & verify
Artificial intelligence in pharmacy and pharmaceutical sciences: Research output, trends, and thematic priorities - A descriptive analysis of web of science-indexed publications (2010-2025)
The student's course in pharmacy : a series of lectures for the use of drug clerks and home students in pharmacy by Watson, Wm. H. (William Henry), 1829-1913, author. Public domain

In brief

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.

Main points

  1. The analysis identified 9671 Web of Science publications on AI in pharmacy and pharmaceutical sciences from 2010 to November 9, 2025.
  2. Publications from 2020 onward made up 84.9% of the literature.
  3. Drug Design and Modeling accounted for 42.1% of publications, versus 3.6% for Clinical Pharmacology and Precision Therapeutics.
  4. Pharmacy operations and decision support (1.6%), pharmacovigilance (1.2%), clinical trials (0.6%), and manufacturing and supply chain (0.5%) were underrepresented.
  5. The most cited papers were methodological AI studies rather than pharmacy-specific applications.

The problem

Research on AI in pharmacy is heavily weighted toward computational drug design, leaving clinical decision support, pharmacovigilance, and manufacturing comparatively underrepresented.

The 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

  1. Peer-reviewedExploratory Research in Clinical and Social Pharmacy2026-09-21

The debate