The field of healthcare is witnessing significant advancements in predictive modeling, driven by the increasing availability of large datasets and the development of innovative machine learning techniques. Researchers are focusing on creating more accurate and interpretable models that can be used for early risk assessment and diagnosis of various diseases. One of the key trends is the integration of clinical and imaging information to improve prediction accuracy, as well as the use of ensemble learning and hybrid models to enhance performance. Additionally, there is a growing emphasis on explainability and interpretability, with techniques such as shape function branching and shapelet-driven post-hoc explanations being explored. These developments have the potential to revolutionize healthcare by enabling early intervention, improving patient outcomes, and reducing healthcare costs. Noteworthy papers include: An Advanced Two-Stage Model with High Sensitivity and Generalizability for Prediction of Hip Fracture Risk, which proposed a sequential two-stage model that integrates clinical and imaging information to improve prediction accuracy. Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients, which developed a hybrid predictive framework that integrates a large-scale electrocardiogram foundation model with an interpretable XGBoost classifier. Empowering Decision Trees via Shape Function Branching, which proposed a novel generalization of decision trees that enables rich, non-linear partitioning in one split. From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification, which proposed a prototype-driven framework for generating sparse counterfactual explanations tailored to 12-lead ECG classification models. ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models, which presented an innovative framework that segments time series into meaningful shapelet-driven segments and employs Shapley values to assess their saliency.