The field of predictive modeling is witnessing significant developments, driven by the increasing availability of heterogeneous data and advances in large language models. Researchers are exploring innovative applications of these models to improve clinical decision-making, patient outcomes, and personalized care. A key direction is the integration of structured and unstructured data, such as electronic health records and clinical notes, to create more comprehensive and accurate predictive models. Another area of focus is the development of adaptable and interpretable models that can handle complex, real-world challenges and provide actionable insights for healthcare professionals. Noteworthy papers in this area include: The paper on Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models, which presents a novel framework for CVD risk prediction that surpasses established risk scores and standard machine learning approaches. The Medical World Model paper, which introduces a generative simulation approach for tumor evolution and treatment planning, demonstrating state-of-the-art performance in optimizing individualized treatment protocols.