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

Area of Science:

• Pharmaceutical Science
• Computer Science
• Data Science

Background:

• Artificial intelligence (AI) adoption is accelerating across the pharmaceutical industry's value chain.
• While AI offers improved efficiency and cost reduction, its integration presents unique challenges.
• This study examines the technological evolution and current innovation hotspots of AI in pharmacy.

Purpose of the Study:

• To analyze the technological evolution of AI in the pharmaceutical sector.
• To identify contemporary innovation hotspots in AI applications within pharmacy.
• To provide insights into future technological development directions.

Main Methods:

• A multi-source data fusion analysis approach was employed.
• Patented inventions (1990-2024) and research articles (2020-2024) were analyzed.
• Latent Dirichlet Allocation (LDA) topic modeling and CiteSpace knowledge graph construction were utilized for data analysis.

Main Results:

• A significant surge in AI-related pharmaceutical patents over the last five years was observed.
• Distinct evolutionary trends in AI technological topics were identified.
• Research hotspots span the entire pharmaceutical lifecycle, from drug discovery to clinical delivery.

Conclusions:

• Key AI application hotspots in pharmaceuticals include target identification, virtual screening, drug delivery, clinical trials, and pharmacovigilance.
• Precision medicine and explainable AI (XAI)-driven pharmacy modeling are emerging as critical future development areas.
• AI is fundamentally reshaping the pharmaceutical industry and is expected to drive significant future innovation.