Technological Evolution and Hotspot Identification for Applying Artificial Intelligence in Pharmacy Based on Topic...

The current pharmaceutical industry has increasingly adopted artificial intelligence (AI), integrating it across the entire industrial chain. While AI improves efficiency and reduces costs, it also faces challenges. This study explores both the technological evolution and contemporary innovation hotspots of AI in pharmacy.

Methods: This study adopts a fusion analysis of multi-source data, constructing a bidimensional analytical framework based on patented inventions (1990-2024) and research articles (2020-2024) as research objects. The study applies the Latent Dirichlet Allocation (LDA) topic model to analyze the evolution of patent topics and employs CiteSpace to construct keyword knowledge graphs from research articles. By integrating patent and article data to define technical labels, the study identifies research hotspots from the perspective of the pharmaceutical life cycle, enabling cross-validation from both scientific and technical dimensions.

The number of AI-related patents in the pharmaceutical field has grown rapidly over the past five years. Technological topics exhibit a distinct evolutionary trend. Research hotspots span the entire pharmaceutical life cycle, from drug development to clinical delivery. Additionally, potential directions for future technological development have been identified.

Research hotspots in the application of AI in pharmaceuticals include target identification, virtual screening, drug delivery, clinical trials, and pharmacovigilance. Precision medicine and explainable AI (XAI)-driven pharmacy modeling are expected to emerge as key directions for future technological development.

AI has already reshaped the pharmaceutical industry through applications across all stages of the pharmaceutical life cycle. It is poised to attract growing research attention and drive innovative applications in the years ahead.