#41 - If You Cannot Trace The Data, Do Not Trust The Model
What if the biggest risk in clinical AI isn’t the algorithm itself, but the data it was built on? A model can appear accurate, polished, and ready for real-world use while quietly relying on datasets with unclear origins, missing documentation, or hidden flaws. In healthcare, that is more than a technical issue. It is a patient safety issue.
In this episode, we explore data provenance—the essential but often overlooked practice of understanding where healthcare data comes from, how it was collected, what it truly represents, and whether it should be trusted for clinical prediction in the…
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