Data Integrity as the Foundation for AI
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Data Integrity as the Foundation for AI
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Artificial intelligence is advancing quickly into laboratory operations, which is why thinking about the quality and integrity of the supporting data early on is important. In regulated environments, that means understanding where the data came from, how it changes, and how AI-generated insights or recommendations can be traced, reviewed, trusted, and ultimately defended.
In this GEN podcast, LabVantage’s Gary Stimson digs into what it takes to build a data foundation for trustworthy AI. The discussion covers concepts like data lineage, traceability, explainability and governance, and the importance of audit-ready laboratory operations. It also looks at the role of intended use, ongoing monitoring, and human review in deploying AI responsibly.
Hosted on Acast. See acast.com/privacy for more information.