Advances in AI for Medical Imaging: A Review of Machine and Deep Learning in Disease Detection

This review provides an exhaustive overview of the impact of machine learning (ML) and deep learning (DL) methods on medical imaging. This paper focuses on how AI is revolutionizing the field of disease detection and diagnosis. These advances have enhanced the precision and ability to diagnose various medical conditions, including cancer, neurological diseases, and retinal disorders. Autonomous ML and DL techniques have enhanced the accuracy of diagnostic processes while simplifying them, eliminating potential human errors, and supporting better clinical judgment by automating intricate image processing functions. The paper presents a comprehensive analysis of the main techniques of ML and DL, such as Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Numerous case studies have demonstrated the remarkable accuracy of these techniques when compared with traditional diagnostic methods. However, the broad adoption of these techniques in medical imaging faces obstacles because of low data quality, lack of interpretability of models, and the requirement of additional computational resources. These problems can be mitigated by creating interpretable AI systems, optimizing the efficiency of computational resources, and establishing ethical guidelines for utilizing these algorithms in healthcare. The review concludes by evaluating the potential of these technologies to transform individualized treatment and healthcare delivery. Ongoing collaboration among technologists, healthcare practitioners, and policy specialists is necessary to guarantee the responsible assimilation of AI into clinical practice.