Recent advancements in vision-language models (VLMs) have demonstrated remarkable capabilities across diverse domains. In this talk, we explore the effectiveness of VLMs in a transfer learning setting, where a pre-trained model is fine-tuned on domain specific data. We first introduce PaliGemma 2, a state-of-the-art, open weight VLM from Google with detection and segmentation capabilities. We then present its application to chest X-ray (CXR) interpretation, detailing the adaptation process that achieved state-of-the-art performance on radiology report generation. This talk highlights the potential of VLMs to democratize access to advanced medical image analysis tools with practical guidance on how to leverage them.