Downstream Medical

Downstream medical applications leverage machine learning to analyze medical images and electronic health records (EHRs), aiming to improve diagnostic accuracy, treatment planning, and patient care. Current research focuses on developing robust and efficient models, including diffusion models, normalizing flows, and large vision models like Segment Anything Model (SAM), often incorporating self-supervised or active learning techniques to address data scarcity and annotation challenges. These advancements are crucial for enhancing the reliability and generalizability of medical AI, ultimately leading to more effective and personalized healthcare.