Recently, Multimodal Large Language Models (MLLMs) have demonstrated strong visual understanding and reasoning abilities across vision-language tasks. However, conventional Visual Question Answering (VQA) pipelines do not fully exploit the visual features and medical knowledge captured by MLLMs, leading to limited performance in zero-shot medical disease recognition. To address this issue, we propose LLaVA-RadZ, a simple yet effective framework for zero-shot radiology recognition. LLaVA-RadZ introduces Decoding-Side Feature Alignment Training (DFAT), which leverages decoder-side representations with modality-specific tokens for image-text alignment, and a Domain Knowledge Anchoring Module (DKAM), which incorporates disease-aware semantic anchors to mitigate the category semantic gap. Extensive experiments show that LLaVA-RadZ substantially improves the zero-shot disease recognition ability of MLLMs and achieves competitive performance compared with well-established CLIP-based radiology recognition methods.