The Impact of Artificial Intelligence on X-Ray Interpretation and Diagnostic Accuracy

This paper reviews the transformative impact of Artificial Intelligence (AI) on X-ray interpretation and diagnostic accuracy in medical diagnostics. AI, particularly through deep learning models like Convolutional Neural Networks (CNNs), addresses cognitive and systemic bottlenecks in human-based analysis. Key applications include Computer-Aided Detection (CAD) systems and AI-driven workflow optimization tools. AI models often achieve diagnostic accuracy comparable or superior to human radiologists, with improvements in sensitivity and specificity for specific tasks, particularly in mammography screening. However, significant limitations persist, including false positives, lack of generalizability across different clinical settings and patient populations, and the "black box" nature of many algorithms. The paper critically examines the ethical considerations of deploying AI in clinical practice, focusing on algorithmic bias, data privacy, and accountability frameworks. The future of radiology lies in a collaborative human-AI paradigm, where AI augments radiologist capabilities while clinicians retain responsibility for complex interpretation, contextual understanding, and patient care. Successful and ethical integration of AI into routine radiography requires continuous validation against strong clinical ground truths, transparent regulatory oversight, and a sustained commitment to interdisciplinary research.