Researchers developed CAPT, a method that lets newer general-purpose language models borrow clinical knowledge from older specialized medical models without requiring expensive retraining. The approach uses contrastive decoding to selectively inject clinical terminology and reasoning patterns while keeping the newer model's superior language abilities, and it works even when the models use different vocabularies. In tests on clinical tasks, CAPT beat existing ensemble methods by significant margins and produced outputs physicians rated as more accurate and clinically appropriate.

Researchers developed CAPT, a method that combines a new general-purpose language model with an older clinical model without requiring retraining. The technique uses contrastive decoding to blend clinical knowledge into the newer model while keeping its reasoning abilities intact, and it outperformed existing ensemble methods by 17-41% on clinical tasks. This approach helps hospitals with limited computing resources adopt newer models without the expensive process of retraining them on clinical data.