The field of medical imaging and clinical trials is witnessing significant advancements in synthetic data generation, enabling the creation of high-fidelity datasets that can supplement or replace real data. This development has the potential to address longstanding challenges such as data privacy, accessibility, and scalability. Researchers are exploring innovative approaches, including generative models and hyperparameter optimization, to improve the quality and utility of synthetic datasets. Notably, the integration of domain-specific constraints and knowledge is proving crucial in ensuring the clinical validity and reliability of generated data. Noteworthy papers in this area include:

• Lesion-Aware Generative Artificial Intelligence for Virtual Contrast-Enhanced Mammography in Breast Cancer, which proposes a novel framework for generating high-fidelity dual-energy subtracted images without the need for contrast agents.
• Phenotype-Guided Generative Model for High-Fidelity Cardiac MRI Synthesis, which presents a two-stage approach for generating diverse cardiac MRI data that captures a wide spectrum of cardiac health status.