AI-Powered Framework for X-Ray and MRI Image Enhancement Using Python: A Novel Approach to Reducing Noise, Blur, and Artifacts for Superior Diagnostic Precision

The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic accuracy, particularly for X-ray and MRI scans. This paper presents a novel AI-driven framework for image enhancement that reduces noise, blur, and artifacts, significantly improving diagnostic clarity. Using Python, the proposed system leverages state-of-the-art deep learning models, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), to achieve diagnostic accuracy exceeding 95%. In experiments, the system processed X-ray images with a diagnostic accuracy of 96.3% and MRI scans at 97.1%, outperforming traditional enhancement methods. The framework reduces noise by 25% and minimizes artifacts by 30%, resulting in clearer, more reliable images suitable for automated analysis. Additionally, the system operates at 35 FPS, ensuring real-time clinical applications. Despite these successes, challenges in model scalability and adaptability to diverse datasets persist, with future work focusing on further optimization and integration into clinical workflows for faster decision-making. This research highlights the transformative potential of AI for advancing medical diagnostics.