The field of deep learning is moving towards developing more robust and generalizable models for medical imaging and time series analysis. Researchers are exploring new architectures and training strategies to improve the performance of models on diverse datasets. One key direction is the use of few-shot learning and in-context learning to adapt models to new tasks and domains with limited labeled data. Another area of focus is the development of universal models that can be applied to different types of data, such as electrocardiogram (ECG) recordings and retinal optical coherence tomography (OCT) images. These advances have the potential to improve the accuracy and efficiency of medical diagnosis and treatment. Notable papers in this area include uPVC-Net, which achieved state-of-the-art performance on ECG-based premature ventricular contraction detection, and PictSure, which demonstrated the importance of pretraining embeddings for in-context learning image classifiers. Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection also showed promising results in using foundation models for subset selection, outperforming traditional methods on fine-grained datasets.