The field of cancer care and disease progression modeling is witnessing significant advancements with the integration of artificial intelligence (AI) and machine learning (ML) techniques. Recent research has focused on developing innovative frameworks for remote patient monitoring, prognostic event modeling, and disease subtype inference. These approaches aim to improve patient outcomes by providing early warnings, identifying key predictive features, and uncovering complex interactions between clinical and demographic factors. Notably, the use of multi-modal AI frameworks has shown promise in forecasting adverse events and enhancing patient support. Furthermore, structured prognostic event modeling has improved survival prediction in cancer by efficiently capturing critical prognostic events. The association between healthcare teamwork and patient outcomes has also been explored, highlighting the potential role of human collaboration in shaping patient survival. Additionally, Bayesian event-based models have demonstrated robust performance in disease subtype and stage inference. Some particularly noteworthy papers include: the development of a multi-modal AI framework for remote patient monitoring, which achieved an accuracy of 83.9% in forecasting adverse events. The introduction of SlotSPE, a slot-based framework for structural prognostic event modeling, which outperformed existing methods in 8 out of 10 cancer benchmarks. The SmartAlert system, a machine learning-driven clinical decision support system, which reduced unnecessary repeat testing by 15% in a randomized controlled pilot.