Aim: This study analyzed the landscape of drug delivery research with a bibliometric approach, focusing on Artificial Intelligence (AI) using data from the Web of Science. Materials and Methods: The analysis included 20 years from 1992 to 2023, using R Studio and VOS Viewer for data analysis. Results: Findings reveal a surge in interest post-2012, with 254 articles published in 182 journals, and the average age of articles remarkably low at 2.04 years, indicating the dynamism of the field. The study identified China as the leading contributor, followed by notable contributions from the United States of America and India. The National University of Singapore emerged as the most productive institution. The International Journal of Pharmaceutics was highlighted as the most impactful journal. The co-word analysis revealed thematic clusters such as AI in fabrication, formulation design, nanoparticles, and prediction of in vivo drug delivery, with niche themes like targeted delivery and toxicity prediction. The thematic map showcases 17 clusters, providing a nuanced understanding of the evolution of research themes over time. The hierarchical clustering dendrogram reveals that AI has been primarily applied in modeling nanoparticulate drug delivery for cancer. As a result, AI-based models for predicting drug delivery for other diseases remain lacking. Conclusion: This bibliometric study provides a holistic view of AI in drug delivery research, identifying trends, collaborations, and emerging themes. The remarkable growth observed in recent years indicates the promising future of AI-driven drug delivery research.
