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

Abstract

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, authors, research themes, citation impact, and funding organizations were analyzed using descriptive statistics, keyword analysis, and descriptive network visualization. Results A total of 9671 publications were identified. Research output increased markedly over the study period, with publications from 2020 onward accounting for 84.9% of the literature. Original research articles published in English predominated. The Journal of Chemical Information and Modeling, Journal of Pharmaceutical Sciences, and Briefings in Bioinformatics were the leading publication venues. China, India, and South Korea were the most productive countries based on corresponding author affiliation. Drug Design and Modeling was the predominant thematic domain, encompassing 42.1% of publications, whereas Clinical Pharmacology and Precision Therapeutics represented 3.6%. Pharmacy Operations and Clinical Decision Support (1.6%), Pharmacovigilance and Medication Safety (1.2%), Clinical Trials and Drug Development (0.6%), and Pharmaceutical Manufacturing and Supply Chain Management (0.5%) were comparatively underrepresented. Citation analysis indicated that the most influential publications were primarily methodological AI studies rather than pharmacy-specific applications. Conclusions AI-related research in PPS has expanded rapidly but remains predominantly focused on computational drug discovery and preclinical applications. Greater emphasis on clinical implementation, patient-centered pharmacy practice, pharmacovigilance, and broader geographic participation is needed to support equitable translation of AI into pharmaceutical care.