The field of Alzheimer's disease diagnosis and PET imaging is moving towards the development of more accurate and accessible diagnostic tools. Researchers are exploring the use of multimodal neuroimaging fusion, language-enhanced generative modeling, and collaborative attention mechanisms to improve diagnostic performance. The integration of large language models and multimodal information fusion is also being investigated to synthesize PET images from MRI and blood biomarkers. Furthermore, retrieval-augmented systems are being proposed to enhance contextual understanding and identify early signs of cognitive decline. Notable papers in this area include: PETAR, which introduces a 3D mask-aware vision-language model for spatially grounded report generation. Language-Enhanced Generative Modeling for PET Synthesis, which develops a language-enhanced generative model to synthesize PET images from MRI and blood biomarkers. BRAINS, which proposes a retrieval-augmented system for Alzheimer's detection and monitoring using large language models and case retrieval modules.