The field of clinical data analysis is moving towards a more rigorous and statistically sound approach to ensuring the reliability and safety of AI-based systems. There is a growing recognition of the need for post-deployment monitoring and validation of AI-extracted data, particularly in the context of electronic health records. Researchers are developing innovative frameworks and tools to address these challenges, such as stream-first data quality monitoring models and comprehensive validation frameworks for large language models. These advancements have the potential to significantly improve the accuracy and trustworthiness of clinical data and AI-powered evidence generation. Notable papers in this area include: EvidenceOutcomes, which presents a novel dataset for clinically meaningful outcomes extraction, and Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health, which argues for the importance of statistically valid testing frameworks in clinical AI. Additionally, Stream DaQ and Ensuring Reliability of Curated EHR-Derived Data also present significant contributions to the field.