Radiological image quality is the cornerstone of accurate medical diagnosis. Minor technical flaws—such as motion blur, improper radiation exposure, or truncated anatomical coverage—frequently cause diagnostic delays or necessitate re-imaging. In this environment, artificial intelligence (AI) has emerged as an automated frontline defense, analyzing scans before they even reach the radiologist's workstation. Driven by deep-learning algorithms, AI systems evaluate radiological images (X-ray, CT, MRI) within seconds of acquisition inside the scanning suite. By identifying technical imperfections immediately, the software alerts the technologist to adjust positioning or retake the scan while the patient is still present. This minimizes operational delays and eliminates the inconvenience of patient recalls. Beyond technical quality assurance, AI plays a critical role in clinical triage. Processing thousands of scans daily, these systems scan for subtle indicators of life-threatening conditions—such as acute intracranial hemorrhage or pneumothorax—automatically prioritizing these urgent cases at the top of the radiologist’s reading queue. Rather than replacing the radiologist, AI acts as a vital collaborative tool. Serving as an objective, highly sensitive safeguard, it drastically reduces technical human error, establishing a faster, safer, and more efficient standard of patient care. Almustaqbal University – The First University in Iraq