Protected Healthcare Imaging Exchange Methods: Digital Watermarking Techniques, Challenges, and Future Directions

The Internet has made it easy for healthcare professionals to use medical documents. To protect sensitive patient information and allow collaboration, it is important to securely send and manage medical images. This study examines different approaches for secure medical data sharing, emphasising their benefits and drawbacks. We put these methods into two groups: centralised methods, like encryption and watermarking, and distributed methods, like blockchain and federated learning. This study also looks at how medical image watermarking techniques have changed over time, from simple methods to more advanced AI-based systems. White boxes are simple and easy to understand, but deep learning models are black boxes that are more flexible and strong. This analysis underscores the necessity of incorporating contemporary technology to tackle the escalating complexity of threats, whilst maintaining the diagnostic fidelity of medical images. Additionally, our research offers a detailed classification of watermarking techniques and delineates prospective research avenues, enriching the ongoing dialogue regarding the improvement of data security in medical imaging.