Leveraging AI to achieve SDTM compliant outputs

The application of Artificial Intelligence (AI) in the production of Study Data Tabulation Model (SDTM) has the potential to revolutionize clinical trial data management by drastically increasing efficiency and reducing errors. The primary goal is to achieve a significant increase in output quality with less effort through the use of AI and automation. 

The process begins with the protocol design, where AI can assist in providing the right metadata be used downstream for study build and SDTM mapping. In the study build phase, AI can use a metadata repository (MDR) to create a full study in an Electronic Data Capture (EDC) system with visits, forms, data elements, rules etc. This approach not only reduces manual intervention significantly, but also ensures consistency across studies. The final step involves the automated generation of SDTM datasets where AI uses a library of transformations to generate SDTM datasets based on the protocol and the clinical database. This allows users to select and apply transforms based on protocol requirements, further streamlining the process. 

Despite holding great promise for SDTM automation, AI is not yet fully mature. Human review is essential to ensure coherence and quality control. Nevertheless, as technology continues to evolve, we can expect AI's role in SDTM production to become increasingly significant to achieve high efficiency and compliant outputs.