Don't be data poor

Official Schedule Context

What do you do when the data you most need to train and evaluate on is the data you're least allowed

to keep? It's a bind for anyone building AI in a high-stakes vertical: the cases that would teach

your model the most — the rare, the messy, the sensitive — tend to be the ones wrapped in the

tightest constraints. In healthcare it's near-absolute. PHI can't be retained, reused, or

transformed, so your long-lived datasets can't contain real patient data at all. Synthetic data is

the obvious escape hatch, but it has its own trap: synthetic records tend to look synthetic, and a

model that passes on fake-looking data tells you nothing about the real thing. So the bar isn't

generating data — it's generating data faithful enough to trust. This talk is how we got there. Ask

an LLM for a full case in one shot and you get something generic and averaged-out — models are worse

at inventing convincing, specific detail than you'd expect. We present our synthetic generation

pipeline (and the process around it) that enabled us to create golden datasets at scale. The

pipeline features a coarse-to-fine process that enriches a patients medical history layer by layer,

with a human in the loop hooks to steer the narrative at each step. You'll leave with ideas on how

to build your own synthetic data generation capabilities and how to build a data pipeline your

domain experts actually enjoy owning.

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