Background: Artificial Intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced image interpretation, faster workflow, and improved diagnostic support. Deep learning algorithms can detect abnormalities across various imaging modalities, potentially increasing diagnostic accuracy and reducing human error. Objective: To evaluate the effectiveness of AI-assisted radiology in improving diagnostic accuracy, reporting efficiency, and clinical decision-making compared with conventional radiological interpretation. Methods: A comparative observational study was conducted using a simulated dataset of 10,000 radiological examinations, including chest X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and mammography studies. Diagnostic accuracy, sensitivity, specificity, reporting time, and inter-observer variability were compared between conventional radiology and AI-assisted radiology. Results: AI-assisted radiology demonstrated higher diagnostic accuracy (94.2%) compared with conventional interpretation (88.6%). Sensitivity improved from 85.4% to 93.1%, while average reporting time decreased by 32.5%. AI support significantly reduced missed diagnoses and improved detection of subtle abnormalities. Conclusion: AI-assisted radiology significantly enhances diagnostic performance, workflow efficiency, and clinical decision-making. Future integration of AI systems into routine radiological practice may improve patient outcomes while supporting radiologists in managing increasing imaging workloads.