| James Joseph |
We propose transitioning from traditional metadata-driven automation, relying on metadata repositories and rules maintenance, to an innovative approach leveraging LLMs and graph databases. We highlight the inefficiencies of maintaining rules for metadata-driven automation and suggest a more dynamic and adaptable solution employing LLMs and graph databases to understand, analyze, and document clinical trial codebases. This new approach promises improved code quality, reusability, and alignment with key study documents, resulting in more efficient and maintainable software systems for clinical trials. |