To analyze the current status, hotspots, and trends of artificial intelligence (AI) technologies applied in the field of drug clinical trials, providing a reference for the application of AI technologies in this domain.

A visual analysis of literature on AI applications in drug clinical trials was conducted using CiteSpace across Chinese databases (CNKI and Wanfang) and the English database (Web of Science).

A total of 135 Chinese and 122 English publications were included. The volume of Chinese publications on AI technologies in drug clinical trials exhibited fluctuating growth, while international English publications demonstrated steady and rapid growth. The institution with the highest number of domestic publications was the China National Medical Products Administration, while internationally, it was Harvard University. Collaborative networks among researchers were relatively decentralized both domestically and internationally. Domestic collaborations primarily occurred among pharmaceutical companies, exhibiting a fragmented overall structure. International collaborations were concentrated among universities, with regional clusters of intensive collaboration. Keyword co-occurrence and clustering analyses revealed both commonalities and differences in AI applications between domestic and international reaserch. Currently, machine learning technologies represent a hotspot in both domestic and international research. Applications in participant recruitment are observed globally, and machine learning is leveraged for data analysis to optimize clinical trials, assess outcomes, and diagnose diseases. Domestically, AI applications focused on clinical trial data management, information system design, and implementation. Internationally, key areas included patient recruitment, disease diagnosis within trials, and biomarker prediction. Domestic research emphasized compliance in AI applications, whereas international research prioritized the refinement of AI algorithmics.

The application of AI in drug clinical trials is evolving from foundational data processing toward complex scenario implementation. Future research may advance in areas such as standardization of data formats, data compliance, and the application of AI in complex clinical trial scenarios.