A 10-year-old CNN beats GPT-6 Astra and Claude Fable 5.1 at skin-cancer diagnosis
Dr Magnus Lynch's test of 200 biopsy-proven lesions found Inception v3, a 2015 CNN, named the right diagnosis 62% of the time, against 42% for GPT-6 Astra and Claude Fable 5.1.
A 10-year-old convolutional neural network beat two of the most capable models of October 2026 at diagnosing skin cancer, according to a comparison by Dr Magnus Lynch, a consultant dermatologist at Guy's Hospital. Lynch writes that the LLMs were "less accurate than I expected" and that, "to my surprise," the old CNN was superior.
The test used 200 biopsy-proven lesions from the MILK10k dataset, half of them unsafe to discharge. Inception v3, first published by Google in 2015, named the right diagnosis for 125 (62%). GPT-6 Astra got 84 and Claude Fable 5.1 got 85 (both 42%). With a second choice allowed, the CNN reached 173, against 134 and 138.
Melanoma was the weak spot for everyone: the CNN named 13 of 30, Claude 11, GPT-6 Astra 8. GPT-6 Astra did score 25 of 25 on basal cell carcinoma, but it also called 75 lesions basal cell carcinoma when 25 were.
The CNN was fine-tuned in about 20 minutes on a single H100 GPU, and Lynch says it is small enough to run in a phone browser.
Diagnosis is not safe triage. At its preset threshold, the CNN discharged 18 of 30 melanomas, versus three for GPT-6 Astra and four for Claude Fable 5.1. At matched thresholds, it discharged one or two.
The context: a bigger general-purpose model is not automatically a better specialist. My take: one run per case. Read the confusion tables before the headline.
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Meanwhile at the anchor desk
A 2015 model humbling two of the most capable systems of October 2026 is the greatest comeback story in medicine, and it trained in twenty minutes! Somebody sign this CNN immediately.
Fine-tuned on one H100, runs in a phone browser via WebGPU, nothing uploaded. The old CNN brought a deployment story, not just a score.
