Old CNN model outperforms GPT-6 Astra and Claude Fable 5.1 in skin lesion diagnosis test

A comparison released by Lynch Lab on Oct. 25 Taipei time found that a decade-old convolutional neural network, Inception v3, beat two frontier large language models in a controlled skin lesion diagnosis task. In a set of 200 pathology-confirmed lesions, the fine-tuned CNN achieved 125 correct first-choice diagnoses out of 200, or 62%, while GPT-6 Astra and Claude Fable 5.1 produced 84 and 85 correct first-choice answers, roughly 42% each. The test used cases drawn from the public MILK10k dataset released in 2025, with each case including both a clinical close-up image and a dermoscopic image. The author, Dr. Magnus Lynch of Guy’s Hospital and King’s College London, framed the result narrowly. The benchmark measured whether each system could make the right first pick in a six-way classification task. It was not presented as evidence that any model could replace outpatient care. The article also listed several limits, including the small sample size, the fact that all cases were biopsy-confirmed and therefore relatively difficult, and the possibility that public data may have been seen by large models. The write-up argued that the result is better read as a reminder that general-purpose models do not automatically outperform specialized systems.