The transition from 2D to 3D medical imaging presents unprecedented challenges in data analysis and interpretation, particularly due to domain shifts and the complex temporal nature of patient data. We introduce 3DLVR, a groundbreaking framework that leverages advanced domain adaptation techniques to bridge this dimensional gap in automated radiology report generation. At the heart of 3DLVR lies a novel cross-attention-based multi-modal fusion module, 3DLR, which seamlessly integrates longitudinal patient data to adapt to evolving medical conditions and imaging characteristics. Our approach utilizes optimal transport theory to formulate the domain adaptation problem, ensuring robust performance across diverse patient populations and imaging protocols. Experiments on the CT-RATE dataset demonstrate 3DLVR’s superior adaptability, with a great improvement in cross-domain performance compared to existing methods. Furthermore, our framework exhibits remarkable generalization capabilities. By advancing the frontier of domain adaptation in medical AI, 3DLVR not only enhances the accuracy and relevance of automated reports but also paves the way for more personalized and adaptive healthcare systems. This work represents a significant step towards realizing the potential of 3D medical imaging in the era of precision medicine, with broad implications for improving diagnostic accuracy, treatment planning, and patient outcomes.