How Can Generative AI Transform Clinical Data Management?

Clinical and scientific data collected from Electronic Data Capture (EDC) systems and third-party sources (e.g., lab data, biomarker, PK/PD data) during clinical trials involves intensive data exploration, visualization, and analysis for ensuring data quality and patient safety.

Current practices in clinical data management involve the complex process of data ingestion from multiple sources and often rely on data review tools. These tools require end users to navigate through numerous listings, reports, and dashboards to access the necessary insights. Unfortunately, creating these reports and dashboards demands significant time and effort for each study as they are integrated within study-specific data containers. Additionally, data cleansing involves extensive manual work, and gaining valuable insights from data using disparate analytics and visualization tools is challenging.

To address these challenges, generative AI technology emerges as a promising solution for revolutionizing clinical data management. By harnessing the power of generative AI in the analysis of clinical trial data, we can achieve more streamlined and accurate data ingestion, standardization, exploration, and analysis processes. Machine learning models empowered by generative AI can provide a faster setup of study data quality framework, automatic transformation of raw study data to data review models, identification of anomalies and discrepancies, generation of query texts, and automation of review and medical monitoring workflows.

In this session, we will provide an overview of Language Models (LLMs), which form the building blocks of generative AI. We'll explore how sponsors are leveraging Generative AI models in various use cases within clinical data management. This will include discussing the outcomes of different experiments and the insights gained from proof of concepts. Additionally, we'll examine how sponsors are strategizing to transition these use cases into production while addressing regulatory and data security requirements. The session will also offer an end-to-end system view, presenting the future state of clinical data management empowered by generative AI.

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