emerges as a critical component in ensuring the secure exchange of medical information within telemedicine frameworks.  
The primary aim of image watermarking in the field of telemedicine is twofold: to authenticate the origin and integrity of medical images and to protect patient confidentiality [1]. By embedding imperceptible digital watermarks within medical images, healthcare providers can verify the authenticity of images, ensuring they have not been tampered with during transmission or storage. Additionally, watermarking serves as a means to preserve patient privacy by embedding encrypted patient information directly into medical images, facilitating secure data exchange while mitigating the risk of unauthorized access. Within the realm of image watermarking, various techniques and methodologies have been developed to address specific security requirements and operational constraints. These techniques can be broadly categorized into spatial and frequency-based approaches, each offering unique advantages and trade-offs. Spatial domain techniques involve directly modifying pixel values within the image to embed the watermark, with methods like least significant bit (LSB) insertion being prominent examples [5]. On the other hand, frequency-based techniques operate by embedding the watermark with frequency transforms of the image, such as the Discrete Cosine Transform (DCT), the DWT, Singular-Value Decomposition (SVD), and so on [6; 7; 8]. These frequency domain methods offer enhanced robustness against certain types of attacks while preserving image quality, making them well suited for applications in telemedicine where image fidelity is crucial for accurate diagnosis. In the subsequent sections, we will delve into the intricacies of image watermarking techniques, exploring their strengths, weaknesses, and applicability in securing medical images within telemedicine frameworks. By understanding the nuances of watermarking methodologies, we aim to shed light on effective strategies for enhancing data security and privacy in the digital healthcare domain.  
The proposed medical image watermarking technique, utilizing a combination of the Discrete Wavelet Transform (DWT) and gradients, significantly contributes to sustainability in healthcare. This method ensures patient privacy by offering a secure and reliable way to watermark images, preventing unauthorized access to sensitive data. Additionally, the DWT and gradient approach enhances image robustness and integrity, leading to accurate diagnosis and minimizing the risk of data breaches. Furthermore, by embedding watermarks securely, this technique reduces the need for repeated imaging procedures due to lost or corrupted images. This translates to lower resource consumption and a smaller environmental footprint for healthcare practices. The main contributions of the present work are as follows:  
* By effectively combining the DWT and gradients, the proposed method achieves superior robustness compared to existing techniques.
* We employ image gradients to provide a \"topological map\" of the image, aiding in the identification of areas susceptible to disruption
* The proposed method leverages the relationship between the gradient direction and magnitude to construct a robust histogram for feature extraction. This approach ensures reliable feature recovery even after attacks on the watermarked medical image.
* The method is meticulously designed to strike a balance between robustness, imperceptibility, and computational efficiency, making it well suited for practical applications.  
The remainder of the paper is organized as follows: Section 2 reviews existing watermarking techniques. The core of our proposed method is detailed in Section 3. Section 4 demonstrates the effectiveness of our approach through experimental results. Finally, we conclude the paper and outline potential areas for future research.