Clinical Datasets

Clinical datasets are collections of patient information used to train and evaluate machine learning models for various healthcare applications, such as diagnosis, prognosis, and treatment planning. Current research focuses on addressing challenges like data privacy, heterogeneity, and class imbalance through techniques such as synthetic data generation, federated learning, and the development of robust model architectures including transformers, random forests, and Bayesian neural networks. These advancements aim to improve the reliability and generalizability of AI models in healthcare, ultimately leading to more accurate and efficient clinical decision-making.