Artificial intelligence (AI)-aided clinical data management: Applications, human-in-the-loop workflows, and regulatory considerations.

Clinical data management (CDM) is central to the quality of clinical research. In Japan, CDM faces a shortage of qualified personnel, particularly in academic research organizations (AROs), as well as increasing data volume and complexity. Rapid advances in artificial intelligence (AI), especially large language models, have therefore attracted attention as a way to support CDM. This review summarizes domestic and international examples of AI utilization in CDM-related tasks, including data cleaning, medical coding, and query generation. Across the cases reviewed, a common implementation principle emerged: a human-in-the-loop design in which AI performs initial processing or detection, while final judgment remains with human personnel. This design is especially relevant to AROs, where high data quality must be maintained with limited CDM human resources. Regulatory frameworks, including ICH E6 (R3) and the FDA-EMA Guiding Principles, are beginning to address AI use, but how AI-aided processes should be handled under Good Clinical Practice remains under discussion. Comprehensive risk mitigation is therefore essential. AI and data are interdependent: better data improve AI performance, and better AI can further improve data quality. The shift from manual processes to human-AI collaborative workflows is likely to accelerate, and CDM must develop the technical, regulatory, and risk-management frameworks needed to support that transition.