CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding
This addresses the critical issue of inaccurate, unsupported descriptions in radiology AI, which poses serious risks in medical applications, by providing a lightweight solution to mitigate hallucinations.
The paper tackles the problem of medical hallucinations in radiology multimodal large language models (MLLMs) by proposing Clinical Contrastive Decoding (CCD), a training-free inference framework that improves clinical fidelity, resulting in up to a 17% improvement in RadGraph-F1 on the MIMIC-CXR dataset.
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 Decoding (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.