AI is transforming clinical research by enhancing data analytics, streamlining documentation processes, and improving patient matching for clinical trials. For instance, AI-driven analytics help researchers sift through large datasets efficiently, uncovering insights that might otherwise be missed. Additionally, AI tools reduce the administrative burden on healthcare professionals by automating documentation and charting tasks.

The FDA is actively engaging with AI's potential in healthcare by outlining its applications in drug development through discussion papers. These documents emphasize the importance of human-led governance, data quality, and model development standards. The FDA is also considering a risk-based approach to AI use, ensuring accountability and transparency in its applications.

Future applications of AI in clinical research include expediting the IND application process, generating study protocols from existing literature, and creating digital twins of patients for personalized treatment predictions. These advancements aim to improve trial success rates, enhance patient safety, and streamline regulatory processes, ultimately leading to more effective healthcare solutions.