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A fine-tuned, ten-year-old image-classification network outperformed two frontier language models in an evaluation of 200 biopsy-proven skin lesions, according to Lynch Lab. The Inception v3 model correctly diagnosed 62% of cases, compared with 42% for each LLM.
Why it mattersThe result is a reminder that a newer, broader model is not automatically the better tool for a specialised medical task. It is a small evaluation using a public dataset, not evidence that the network is ready for clinical use. In this particular contest, however, the old specialist had the better scorecard.
Discuss: For medical image diagnosis, should developers favour specialist models with stronger task-specific results, or general-purpose models that can handle more than one job?
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