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Dermatologist: 2015 CNN beat two frontier chatbots on 200 skin lesions
On Lynch Lab, dermatologist Magnus Lynch reports a fine-tuned Inception v3 network named the right diagnosis on 125 of 200 biopsy-proven lesions, versus 84 and 85 for GPT-6 Astra and Claude Fable 5.1.
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A dermatologist at King's College London reports that a 2015 convolutional neural network, fine-tuned on public skin images, named the correct diagnosis more often than two general-purpose chatbot models on 200 biopsy-proven lesions1.
Writing on Lynch Lab on Oct. 25, Magnus Lynch said he showed each case as a clinical photograph plus a dermoscopic image to GPT-6 Astra and Claude Fable 5.1, and to Inception v3 pretrained on everyday photos then fine-tuned on 68,011 ISIC Archive images with the test set held out1. The lesions were a random draw from MILK10k, a 2025 International Skin Imaging Collaboration dataset: 30 melanomas, 25 basal cell carcinomas, 25 squamous cell carcinomas, 20 actinic keratoses, 50 moles and 50 seborrhoeic keratoses, all confirmed on histology1.
The CNN's first-choice diagnosis matched histology on 125 of 200 cases (62%), versus 84 and 85 (42%) for the two chatbots1. Allowing a second choice, the counts were 173, 134 and 1381. Melanoma first-choice hits were 13 of 30 for the CNN, 8 for GPT-6 Astra and 11 for Claude Fable 5.11.
Asked whether a lesion could be discharged with no treatment, biopsy or follow-up, the chatbots kept 97 and 96 of 100 lesions Lynch classed as unsafe, but discharged 3 and 4 of 30 melanomas1. At its preset threshold the CNN kept 77 of those 100 and discharged 18 melanomas; Lynch wrote that matching the chatbots' caution would cut melanoma discharges to 1 or 21.
He said the chatbots overused favourite labels: GPT-6 Astra called 75 lesions basal cell carcinoma when 25 were, including 14 of 30 melanomas; Claude Fable 5.1 called 53 actinic keratosis when 20 were1. Of 17 melanomas the CNN missed, it called 8 a mole and 6 a seborrhoeic keratosis1.
Lynch lists limits: 200 cases and 30 melanomas are too few to rank close scores; each chatbot run was once; every lesion had been biopsied, so the set is harder than routine archive images; MILK10k has been public since 2025, so the chatbots might have seen it; and Inception v3 is a baseline, not the ensembles used in regulated skin-cancer software1. Asked as a patient with only the clinical photo, both chatbots advised seeing a doctor in almost every case in a further 100 melanomas1.
The cases, prompts, replies and scoring code are posted as a download; Lynch designed the test and clinical rulings, with analysis drafts produced using Anthropic's coding tool, and scoring done by script against histology1. The account is a lab blog, not a journal paper, and has not been independently replicated here1.
Not medical advice.