Introduction: Medical diagnostics have been transformed via the incorporation of artificial intelligence (AI) into signal processing, which has greatly enhanced the analysis of intricate biomedical data. This review investigates how AI improves medical imaging diagnoses. Methods: A review of the literature was carried out to find the latest developments in AI signal processing applications in the medical imaging field. Techniques such as data fusion, deep learning, and machine learning were assessed based on their diagnostic utility, accuracy, and precision. Results and Discussion: AI has greatly improved the way that Magnetic Resonance Imaging (RMI) and Computed Tomography (CT) signals are processed, making it possible to analyze high-dimensional biomedical data more precisely and effectively. Convolutional neural networks have demonstrated up to 97% segmentation accuracy in brain tumor identification, which greatly facilitates early diagnosis and treatment planning. Generative adversarial networks have enhanced denoising and picture resolution, making it easier to identify minute anomalies in medical imaging. Precise tissue distinction has been made possible by AI-driven segmentation techniques, which are essential for the diagnosis of cancers and neurological disorders. In CT scans, for instance, AI algorithms have attained a 94% accuracy rate in identifying benign tumors from malignant lung lesions. AI-enhanced MRI has also improved the imaging of intricate anatomical components, which helps with the accurate diagnosis of musculoskeletal disorders and cardiovascular diseases. Additionally, early disease diagnosis and therapy planning have been enhanced by AI-driven signal processing. AI systems have proven to be able to cut the number of false positives in cancer detection by 50%, which increases diagnostic confidence and lowers the number of needless biopsies. Conclusions: AI-driven developments in image segmentation and signal processing have improved the precision of diagnoses and made customized treatment plans possible. Further advancements in research and technology progress are expected to augment the efficacy of these techniques in clinical settings.