The field of medical imaging and electronic health records is moving towards more accurate and efficient methods for disease diagnosis and risk prediction. Recent developments have focused on improving the reliability of downstream prediction models by enhancing phenotype refinement and sample selection. Notable papers in this area include:

• A study on automatic segmentation of colorectal liver metastases for ultrasound-based navigated resection, which achieved near real-time results with minimal operator input.
• The introduction of RELEAP, a reinforcement learning-based active learning framework that optimizes phenotype correction through downstream feedback.
• The proposal of UCDSC, an open set uncertainty aware deep simplex classifier for medical image datasets, which effectively rejects samples of unknown classes.