The recent advancements in the research area have shown a strong trend towards the integration of multimodal data and the development of self-supervised and weakly-supervised learning models. These approaches are particularly promising in medical applications, where the complexity and variability of data necessitate sophisticated models that can learn from diverse data sources without extensive manual annotation. The field is moving towards more personalized and context-specific models, which aim to capture individual patient characteristics and treatment responses, as seen in studies focusing on tuberculosis and chronic kidney disease. Additionally, there is a growing emphasis on the robustness and interpretability of models, with innovations like the Decoupled Classifier with Adaptive Linear Modulation (DEAL) and the Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data (TabDeco) addressing issues of group bias and feature disentanglement. These developments not only enhance the accuracy and reliability of predictions but also provide deeper insights into the underlying mechanisms driving the outcomes. Overall, the integration of advanced machine learning techniques with domain-specific knowledge is paving the way for more effective and personalized healthcare solutions.