Data Classification

Data classification in various scientific domains, particularly healthcare, aims to automatically categorize data into meaningful classes for improved analysis and decision-making. Current research focuses on applying and refining machine learning models, including large language models (LLMs) and convolutional neural networks (CNNs), to classify diverse data types such as text (e.g., medical records, social media posts), audio (e.g., surgical feedback), and images (e.g., dermatological images). These efforts are crucial for enhancing efficiency and accuracy in tasks ranging from medical diagnosis and treatment to surgical training and public health surveillance, but challenges remain in addressing issues like data imbalance, model bias, and ensuring the reliability and interpretability of AI-driven classifications.