Bridging the Past and Future of Clinical Data Management: The Transformative Impact of Artificial Intelligence

Szymon Musik,1,2 Joanna Sasin-Kurowska,2 Mariusz Panczyk1 1Department of Education and Research in Health Sciences, Medical University of Warsaw, Warsaw, Poland; 2Clinical Data & Insights, Biopharmaceutical Clinical Operations, R&D, AstraZeneca, Warsaw, PolandCorrespondence: Szymon Musik, AstraZeneca Poland, Postępu 14A Street, 02-676, Warsaw, Poland, Tel +48 536-931-331, Email [email protected]: Effective clinical data management is fundamental to clinical research and regulatory submissions. Modern clinical trials have increasingly adopted web-based electronic data capture (EDC) systems, which enhance data collection efficiency but introduces challenges in data integration and quality. This scoping review explores the transformative role of artificial intelligence and machine learning in evolving CDM into clinical data science. In the review, we followed the PRISMA-ScR guidelines and analyzed the literature from 2008 to 2025 using Scopus, Web of Science, and PubMed databases. A total of 26 papers were included and categorized into those related to clinical data management, natural language processing, and general artificial intelligence/machine learning adoption in clinical data management. The integration shows promise in enhancing data analysis, automating data cleaning, and predicting critical outcomes. The key emerging trends include risk-based quality monitoring, blockchain technology, remote monitoring, and patient-centric approaches involving wearables and mobile applications. The results clearly indicate a substantial increase in data volume in Phase III trials, underscoring the need for advanced technologies natural language processing offers significant potential in interpreting unstructured text data, thereby improving the clinical data management processes. The review concludes on different artificial intelligence/machine learning techniques like natural language processing, predictive analytics, and automation technologies, and their applications in improving data quality and streamlining clinical data workflows.Keywords: clinical data management, artificial intelligence, machine learning, natural language processing, clinical data science, electronic data capture