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Radiology Specialist · Chief Innovation Officer · Radigrade

Before it reachesthe patient,medical AI shouldpass the exam.

An AI report on a radiograph looks the same whether a finding is there or invented, and a confident plan whether the treatment is needed or not. Nothing in the report warns the clinician.

So my work starts with a grade, in public and for no vendor: findings invented counted first, then treatments not needed, against a reference that two oral and maxillofacial radiologists read together. I read every day as one of them, train models on my own reads, and test them on cases they have never seen to learn where they fail. The essays are the field notes.

100,000+ reports signedRadigrade, the registered benchmark, publicCBCTScope shipped, open sourcewhat I’m building →

// Latest writing

// Lab Notes

All entries
  1. The error to fear is the treatment nothing justifies
  2. What the best general model read on a specialist's ruler
  3. "Normal" is not a verdict

// Browse by series

// Agent-native instruments, open source

Both on GitHub; the viewer is citable via DOI.

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