The recent advancements in the research area have primarily focused on enhancing the integration of multimodal data, improving data imputation techniques, and leveraging advanced machine learning models for healthcare applications. A notable trend is the development of models that can effectively handle incomplete or missing data, which is a common challenge in healthcare. These models, often based on transformer architectures, demonstrate superior performance by utilizing masked self-attention mechanisms to process only available data, thereby minimizing biases introduced by imputation methods. Additionally, there is a strong emphasis on the fusion of diverse data types, such as combining histology images with spatial transcriptomics for more accurate anomaly detection in tissues. Another significant development is the application of diffusion models for spatiotemporal data imputation, which have shown to capture complex relationships and improve imputation accuracy. Furthermore, the use of large language models for medical forecasting and anonymization of protected health information has seen innovative approaches, with models fine-tuned on specialized data outperforming general-purpose models. These advancements collectively push the boundaries of what is possible in healthcare analytics, offering more robust, accurate, and scalable solutions for critical tasks such as diagnosis, risk prediction, and patient data management.