Medical Annotation

Medical annotation focuses on creating structured, labeled datasets from unstructured medical data like clinical notes and images to enable the development and evaluation of machine learning models for various healthcare applications. Current research emphasizes leveraging large language models (LLMs) and transformer-based architectures, often combined with techniques like few-shot learning and active learning, to improve annotation efficiency and accuracy across diverse medical domains, including radiology, oncology, and critical care. This work is crucial for advancing the development of AI-powered diagnostic tools, improving clinical workflows, and facilitating large-scale medical research by unlocking the wealth of information hidden within unstructured medical data.