The field of medical imaging and modeling is rapidly evolving, with a focus on developing innovative methods to improve diagnosis, treatment, and drug development. One of the key directions is the use of machine learning and deep learning techniques to enhance image synthesis, segmentation, and analysis. Additionally, there is a growing interest in modeling complex biological systems, such as cellular responses to external stimuli and physiologically based pharmacokinetic modeling. These advances have the potential to revolutionize the field of medicine, enabling more accurate diagnoses, personalized treatments, and improved patient outcomes. Noteworthy papers in this area include:

• A study that proposed a method to generate Digital Contrast CT Pulmonary Angiography from Non-Contrast CT scans, achieving state-of-the-art performance in vessel enhancement and image fidelity.
• A paper that introduced a machine learning framework for predicting perturbation-induced changes in three-dimensional cellular structure, supporting both unconditional morphology synthesis and conditional simulation of perturbed cell states.
• A research that explored data-driven alternatives for Physiologically Based Pharmacokinetic modeling using deep learning, achieving high predictive performance and demonstrating strong potential for data-driven pharmacokinetic modeling.