Ensuring diagnostic excellence in radiology heavily relies on the implementation of robust Quality Assurance (QA) programs. Both conventional and digital radiology systems demand high standards of operational efficiency to guarantee accurate diagnoses and patient safety. In traditional radiology, QA measures mainly focused on the regular maintenance and assessment of equipment such as X-ray machines, film processors, and screen-film systems. Key elements included performance evaluations, image quality assessments, radiation dose monitoring, and consistent personnel training. However, these programs often varied between institutions due to a lack of uniform protocols, occasionally leading to inconsistencies in diagnostic quality.The introduction of digital technologies, including Computed Radiography (CR) and Digital Radiography (DR), has revolutionized radiology, necessitating an evolution in QA practices. In digital radiology, emphasis is placed on evaluating detector performance, ensuring monitor calibration, verifying the integrity of Picture Archiving and Communication Systems (PACS), and maintaining software reliability. Digital QA programs offer significant advantages, such as automated quality checks, centralized data management, and the potential for remote monitoring. Nevertheless, digital systems introduce new challenges, including cybersecurity risks and the need for continuous software updates. International organizations have recognized the need for standardized QA protocols. Guidelines from bodies like the American Association of Physicists in Medicine (AAPM) and the International Atomic Energy Agency (IAEA) provide comprehensive frameworks for quality management in digital imaging. These standards stress the importance of systematic evaluations of exposure indices, image rejection analysis, equipment calibration, and regular technical audits. Comparatively, while both conventional and digital radiology share core QA principles—such as maintaining image quality and ensuring patient safety—digital imaging demands additional measures tailored to electronic systems and data security. Automated QA tools and artificial intelligence (AI)-based systems are increasingly being incorporated to support error detection, optimize image acquisition, and enhance consistency across imaging studies. Emerging trends point towards the integration of AI and machine learning technologies in QA systems, offering the potential to further minimize human error and increase operational efficiency. Moreover, continuous quality improvement (CQI) models, like the Plan-Do-Study-Act (PDSA) cycle, are being increasingly adopted to promote an ongoing commitment to excellence within radiology departments.However, challenges such as resource limitations, inadequate training among radiologic personnel, and the complexity of rapidly evolving digital technologies continue to impede the full realization of QA goals. Addressing these issues requires a strategic approach involving interdisciplinary collaboration, leadership commitment, and a strong culture of quality and safety.