Skin-lesion test separates diagnosis accuracy from discharge risk

Magnus Lynch's October 25 test compares diagnosis with discharge recommendations.

The big change

• What changed: Six-label accuracy favored a convolutional neural network (CNN).
• Why it matters: Correct labels cannot establish safety of recommendations to stop assessment.
• What to watch: The incomplete threshold-matching rule limits discharge comparisons.

What the numbers measure

First-choice accuracy counts correct diagnoses. A discharge label means no further assessment; no patients were discharged.

Of 200 selected, biopsied lesions (100 keep, 100 discharge; 30 melanomas), CNN labelled 125 correctly; author-labelled GPT-6 Astra and Claude Fable 5.1 scored 84 and 85. Each language model was run once per case; exact revisions and run dates are undisclosed.

At its preset threshold, CNN recommended discharge for 18/30 melanomas; language models, three and four. A threshold change improved CNN's count. BIG CHANGE did not rerun it.

MILK10k pairs clinical and dermoscopic images. This cohort cannot establish routine-care performance; public release leaves training exposure unresolved.