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Over the past two decades, computer-aided detection and diagnosis have emerged as a field of research. The primary goal is to enhance the diagnostic and treatment procedures for radiologists and clinicians in medical image analysis. With the help of big data and advanced artificial intelligence (AI) technologies, such as machine learning and deep learning algorithms, the healthcare system can be made more convenient, active, efficient, and personalized. The primary goal of this literature survey was to present a thorough overview of the most important developments related to computer-aided diagnosis (CAD) systems in medical imaging. This survey is of considerable importance to researchers and professionals in both medical and computer sciences. Several reviews on the specific facets of CAD in medical imaging have been published. Nevertheless, the main emphasis of this study was to cover the complete range of capabilities of CAD systems in medical imaging. This review article introduces background concepts used in typical CAD systems in medical imaging by outlining and comparing several methods frequently employed in recent studies. This article also presents a comprehensive and well-structured survey of CAD in medicine, drawing on a meticulous selection of relevant publications. Moreover, it describes the process of handling medical images and introduces state-of-the-art AI-based CAD technologies in medical imaging, along with future directions of CAD. This study indicates that deep learning algorithms are the most effective method to diagnose and detect diseases.