Multimodal large language models (MLLMs) are advancing radiology by combining image and text understanding, but often generate inaccurate or unsupported clinical details—so-called medical hallucinations. Clinical Contrastive Decoding (CCD) is a training-free and retrieval-free inference framework that integrates structured clinical signals from task‑specific radiology expert models. CCD reduces these hallucinations and improves clinical accuracy without changing the base MLLM, offering a practical way to make radiology MLLMs more reliable.