A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis

I am a dermatologist. I tested the ability of GPT-6 Astra and Claude Fable 5.1 to diagnosis skin cancer and found that they were less accurate than I expected. I compared to a 10-year old CNN and to my surprise found that this was superior. This is a melanoma. Figure 1. Invasive melanoma on the ear, under 0.8 mm thick. Both LLMs called it a seborrhoeic keratosis and said it could be discharged. The CNN called it a melanoma. Clinical photograph on the left, dermoscopic image on the right. Images: MILK10k, MILK study team, CC BY-NC. I showed these two images - a clinical photograph and a dermoscopic (skin microscope) image - to GPT-6 Astra and Claude Fable 5.1, two of the most capable AI models available in October 2026. Both said it was a seborrhoeic keratosis, a harmless warty growth, and that the patient could be discharged. I then showed them to fine-tuned version of Inception v3, a convolutional neural network (CNN) first published by Google in 2015 and fine-tuned here on public dermatology images. The CNN got the right answer based on the dermoscopy view. It was one of 200 biopsy-proven lesions I gave to all three. The CNN named the right diagnosis (out of 6 possiblities) for 125 of them (62%). The two frontier LLMs named it for 84 and 85 (42%). A ten-year-old network, fine-tuned for about twenty minutes, beat both of them at recognising skin cancer. The test# The lesions come from MILK10k, a public dataset released through the International Skin Imaging Collaboration in 2025, in which each lesion is photographed twice: a clinical close-up and a dermoscopic image taken through the illuminated magnifying lens dermatologists use. I drew 200 lesions at random, all confirmed by histopathology: 100 that should not be discharged: 30 melanomas, 25 basal cell carcinomas, 25 squamous cell carcinomas (including Bowen’s disease) and 20 actinic keratoses. 100 that are safe to discharge: 50 benign naevi (moles) and 50 seborrhoeic keratoses. Every lesion was biopsied, including the benign ones, so these are harder to diagnose: a mole that somebody decided to cut out is one that worried a clinician. Basal cell carcinoma on the trunk of a 75-year-old man. All three models answered correctly. Invasive melanoma under 0.8 mm thick, with very little pigment. GPT-6 Astra called it a basal cell carcinoma with 95% confidence. Claude Fable 5.1 and the CNN called it a melanoma. Seborrhoeic keratosis on the leg. Both LLMs called it an actinic keratosis and the CNN a squamous …