The recent advancements in the field of synthetic data generation and image synthesis have shown significant promise in enhancing various applications, particularly in biomedical research and robotics. Researchers are increasingly leveraging diffusion models to bridge the gap between synthetic and real-world data, aiming to improve the accuracy and reliability of machine learning models trained on synthetic data. Notably, the use of cascaded diffusion models for synthesizing densely annotated microscopy images has demonstrated improvements in cell segmentation performance, addressing the scarcity of annotated datasets in biomedical research. Similarly, controlled image synthesis models for colonoscopy images are being developed to offer precise control over spatial attributes and clinical characteristics, which is crucial for improving diagnostic tasks. Additionally, virtual staining techniques using diffusion models are being explored to enhance the spatial resolution and morphological contrast in imaging mass spectrometry, potentially expanding the applicability of IMS in life sciences. These developments collectively indicate a shift towards more sophisticated and controlled synthetic data generation methods, which are expected to have a profound impact on various scientific and clinical applications.