CH Health Tech Advisory

Christian Hein · 21 July 2025 · 1 min read

Is bigger always better when it comes to data in AI?

I explore why bigger isn't always better when it comes to data in AI — and why, in healthcare especially, the real problem is data quality, not quantity. Curated, structured, interoperable data is what will make trustworthy clinical AI possible.

Is bigger always better when it comes to data in AI?

Not according to Scott Wu (CEO, @Cognition) on his recent podcast with Harry Stebbings of 20VC: He predicts the future isn't ultra-large datasets, but "a small set of highly curated data for exactly the use case that you care about."

This really hit home. In my work at SNOMED International, I see firsthand how the lack of curated data holds healthcare back.

For years, we've known that healthcare data has a quality problem, not a quantity problem. To build trustworthy clinical AI, we need to move from a sea of unusable information to clean, structured, and interoperable data. That's where standards like SnomedCT come in.

https://lnkd.in/enR3EjXP

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Artificial Intelligence · Machine Learning · Synthetic Data · Digital Health · Medical Terminology / Standards · Innovation Management