The recent advancements in medical image generation and analysis are significantly enhancing the precision and fairness of diagnostic tools. Innovations in diffusion models are leading the way, with notable improvements in generating realistic and diverse medical images. These models are being fine-tuned to address specific biases, such as those related to skin tone and disease representation, ensuring more equitable outcomes in clinical settings. Additionally, the integration of semantic information and class-aware techniques in image synthesis is advancing surgical scene segmentation, providing more accurate and comprehensive datasets for training and validation. These developments not only improve the quality of generated images but also enhance the performance of downstream tasks, such as disease classification and report generation. Notably, the use of synthetic data in combination with real data is demonstrating promising results, suggesting potential applications in reducing data scarcity and improving model robustness.