With the growth of the Internet, medical documents have become widely used by healthcare professionals. Secure transmission and management of medical images are essential to enable collaboration while protecting sensitive patient information. This study analyzes various methods for safe medical data sharing, highlighting their advantages and limitations We categorize these approaches into centralized techniques, such as encryption and watermarking, and distributed methods, such as blockchain and federated learning. Additionally, this research examines the evolution of medical image watermarking techniques, from traditional methods to advanced AI-based systems. While traditional techniques known as white boxes are simple and interpretable, deep learning models are considered black boxes, offering greater robustness and adaptability. This analysis emphasizes the need to integrate modern technology to address the growing complexity of threats, while preserving the diagnostic integrity of medical images. Furthermore, our work provides a comprehensive classification of watermarking methods and outlines future research directions, contributing to the ongoing discourse on enhancing data security in medical imaging

Keywords: Centralized Techniques, Decentralized Techniques, Medical Image, Secure Sharing