AI promised speed, scale, and savings and many labs got quick wins. But the whitepaper points to a hidden cost nobody warned you about:
Your work started looking like everyone else’s.
Most AI design tools are trained on aggregated data from thousands of labs. They optimize for the “average crown”, a design that offends no one and delights no one. That creates a dangerous outcome: when output becomes indistinguishable, the only remaining differentiator is price.
The paper breaks the damage into three things that are easy to underestimate until they’re gone:
The whitepaper’s core idea is simple:
Generic AI asks “What does a crown usually look like?”
Signature-preserving AI asks “What does a crown look like when your lab designs it?”
Signature-preserving AI learns from your historical designs, adapts over time through technician corrections, and can even adjust for dentist-specific preferences.
Before adopting any AI design solution, the paper recommends asking:
This blog is a high-level summary. The full PDF dives deeper into signature-preserving AI, evaluation questions, and why technicians become more valuable, not less, when AI carries your lab’s signature.
