Deploying AI in a GxP environment requires more than the model itself. Recent guidance points to appropriate AI capabilities, lifecycle controls, relevant data, scalable hardware, and a workforce with sufficient AI literacy. #MSBMI #AI #UTHealth #Science #AgenticAI

A consistent theme is that there is no single approach for every AI use case. Controls should reflect uncertainty, risk, complexity, and context, with AI incorporated into existing lifecycle activities and continued dialogue between industry and regulators. #MSBMI #AI #UTHealth #Science

The EU GMP Annex 22 discussion shows how guidance for AI in regulated environments has progressed from early consultation to more detailed expectations. Critical use cases call for appropriate human oversight, while some technologies, including LLMs, remain discouraged for critical use. #AI #MSBMI

The EMA’s approach to AI adoption focuses on three areas: supporting safe and responsible innovation, improving efficiency in regulatory work, and using healthcare data to strengthen evidence based decision making. #AI #UTHealth #MSBMI #STEM #Healthcare #TMC

A risk based approach to AI regulation considers both the credibility of a model’s output and its specific context of use. The evidence needed to establish trust depends on the role of the model and the question it is intended to address. #AI #UTHealth #MSBMI #Healthcare #STEM #Science

The European Commission’s approach to trustworthy AI is built around reliability, lawfulness, and robustness, supported by requirements including human oversight, safety, privacy, transparency, fairness, accountability, and explainability. #EU #Science #MSBMI #UTHealth #AI

As AI adoption grows across life sciences, health agencies are moving from industry consultation toward formal guidance and regulation. At the same time, industry practices are developing to bring together established quality and compliance standards with data science. #MSBMI #UTHealth #AI #Science