CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical
  Contrastive Decoding
Authors: name: Xi Zhang, name: Zaiqiao Meng, name: Jake Lever, name: Edmond S. L. Ho

Multimodal large language models (MLLMs) have recently achieved remarkable
progress in radiology by integrating visual perception with natural language
understanding. However, they often generate clinically unsupported
descriptions, known as medical hallucinations, which pose serious risks in
medical applications that demand accuracy and image-grounded outputs. Through
empirical analysis, we find that prompt-induced hallucinations remain prevalent
in radiology MLLMs, largely due to over-sensitivity to clinical sections. To
address this, we introduce Clinical Contrastive Cecoding (CCD), a training-free
and retrieval-free inference framework that integrates structured clinical
signals from task-specific radiology expert models. CCD introduces a dual-stage
contrastive mechanism to refine token-level logits during generation, thereby
enhancing clinical fidelity without modifying the base MLLM. Experiments on
three datasets and multiple models demonstrate that CCD consistently improves
overall performance on radiology report generation (RRG). On the MIMIC-CXR
dataset, it yields up to a 17% improvement in RadGraph-F1 when applied to
state-of-the-art RRG models. Our approach provides a lightweight and
generalisable solution for mitigating medical hallucinations, effectively
bridging expert models and MLLMs in radiology.