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.

• CAPT combines a new general-purpose LLM with an old clinical model at inference time via contrastive decoding, no retraining needed, even across mismatched vocabularies.
• It beat UniTE by 17.6% and standard proxy tuning by 41.4% on average across six clinical tasks.
• Physician review and token-level analysis showed more clinically specific language, fewer context errors, and better actionable recommendations.
• Lets resource-constrained hospitals adopt newer models without expensive retraining cycles on clinical data.