An LLM wrote a medical image classifier without seeing one image, but 256 labeled scans still beat it on every task
Claude Opus 5.5 and GPT-6 Astra each wrote the weights of a small convolutional network in a single reply, without access to any image, reaching mean test AUC of 0.719 and 0.711 across six MedMNIST tasks, above zero-shot MedSigLIP at 0.588. Yet the same network trained with Adam on 256 labeled images reached a mean AUC of 0.828 and was higher on every MedMNIST task; on the synthetic uterine MRI task Opus reached 0.764 while Astra fell to 0.136 through a systematic error.