OBJECTIVES : Combination of artificial intelligence (AI) and medical big-data is considered as one of the most valuable technique in the 21st century, which was expected to be assisting in medical innovation of healthcare. To apply AI and patient level data in clinical research, a traceable and regulated Data Process & Application Platform (DPAP) was developed. In this study, all published peer reviewed scientific papers based on the AI technology and real-world data (RWD) derived from DPAP were scrutinized, to examine the application of AI and RWD in real-world evidence generation in China.

METHODS : Thirty-eight papers were retrieved by searching literature databases with inclusion criteria of usage of AI technology, RWD and DPAP. All the papers were published in English. Study types and related disease areas were categorized and analyzed.

RESULTS : Among the 38 papers, 24 were medical research, 14 belong to computer science covered topics in natural language processing (NLP) and data standardization, etc. Publications were all indexed by SCI or SCIE, and the impact factors ranged from 1.80 to 36.42. All papers were published in the past 3 years, indicating the RWD and AI technology have been increasingly contributed to improved health outcomes. The clinical research focused on cancers (15 papers), pneumonia (3 papers), diabetes (2 papers), depression, hand, food and mouth disease, and Kawasaki disease. There were 4 retrospective cohort studies and 20 cross-sectional studies. Eight papers reported multi-center clinical studies.

CONCLUSIONS : The big-data and intelligence database platform accelerate and simplified the process of data extraction, analyses, and management. It could rapidly transform clinical data into research conclusions through proper compliance procedures, which has been well recognized by the pharmaceutical industry, reflecting the demand of demonstrating the medical product values in real world settings in China.