Artificial Intelligence (AI) has emerged as a transformative tool in diagnostic imaging, enhancing the accuracy and efficiency of disease detection. Machine learning (ML) and deep learning (DL) algorithms have shown promising results in interpreting radiological images, often matching or surpassing human radiologists in specific tasks. However, the integration of AI into clinical practice raises significant ethical concerns, including data privacy, algorithmic bias, and accountability in medical decision-making. This review examines the current state of AI in diagnostic imaging, evaluates its diagnostic accuracy, and discusses the ethical and regulatory challenges that must be addressed for its sustainable adoption in healthcare.