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.