Multi Center Study

Multi-center studies aim to improve the generalizability and robustness of machine learning models in healthcare by leveraging data from multiple institutions, overcoming limitations of single-site studies. Current research focuses on adapting and applying various deep learning architectures, including transformers and convolutional neural networks (CNNs), to diverse medical image and clinical data for tasks such as disease classification, survival prediction, and biomarker discovery. These studies are crucial for validating AI-driven diagnostic and prognostic tools, ensuring their reliable performance across different clinical settings and patient populations, ultimately leading to improved patient care.