{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Visual analysis of quality management in Chinese drug clinical trials based on CiteSpace and COOC.","author":[{"@type":"Person","name":"Jiayan Xia"},{"@type":"Person","name":"Xuemei Ning"},{"@type":"Person","name":"Lamei Jiang"},{"@type":"Person","name":"Peipei Li"},{"@type":"Person","name":"Jie Huang"},{"@type":"Person","name":"Wenxiu Jiang"},{"@type":"Person","name":"Wen Li"},{"@type":"Person","name":"Cheng Wang"},{"@type":"Person","name":"Linsha Zheng"},{"@type":"Person","name":"Ting Jiang"}],"datePublished":"2025","isPartOf":{"@type":"Periodical","name":"Frontiers in medicine"},"identifier":"https://doi.org/10.3389/fmed.2025.1600915","description":"BackgroundRecently, considerable progress has been made in the quality of clinical trials conducted in China. However, the number of clinical trials conducted in China still falls below the global average standard. This study aims to identify research hotspots, collaborative networks, and evolutionary trends in the field of clinical trial quality management (CTQM) in China through bibliometrics and visual analyses to provide theoretical support and practical references for the optimization of domestipolicies.MethodsA systematic literature search was performed across the CNKI, Wanfang, and VIP databases to clinical trial quality management CTQM-related publications. Bibliometric analysis was conducted using CiteSpace 6.1.R6 and Co-Occurrence 20.5 (COOC 20.5), with key metrics including: annual output, active institutions, core journals, main authors, keywords, and thematic evolution. To capture internationally published works, supplementary searches were executed in Scopus, Web of Science, and PubMed for CTQM publications authored by Chinese scholars. Owing to the limited number of results (6 records), these documents were only included only in the discussion analysis.ResultsA total of 528 articles were retrieved from the field of CTQM. The research process was divided into three periods: the basic standardization period (2003–2012), technology convergence period (2013–2019), and the intelligent transformation period (2020–2024). The theme shifted from the localization of the system to risk management, data management, and ethical governance driven by emerging technologies. The issuing organizations are primarily national-level administrative bodies, showing strong political-academic collaboration but limited cross-system partnerships. Artificial intelligence (AI)-based clinical trial quality management enhances quality control (QC) efficiency; however, it raises concerns about data privacy and ethical disparities.ConclusionChina’s research in the field of CTQM has led to the innovative integration of traditional quality control methods with new technologies. However, insufficient interdisciplinary cooperation and the absence of a data governance system pose ongoing challenges. In the future, it is necessary to build a three-dimensional ecosystem of “policy guidance, technological breakthroughs, and ethical synergy” to promote the rapid development of drug research in China.","url":"https://innscience.ca/en/community/literature/4440f92a-8548-4670-a39b-e83fc12fe619"}