AI-Powered Radiology: Innovations and Challenges in Medical Imaging

Abstract

Artificial intelligence (AI) is revolutionizing radiology by enhancing diagnostic accuracy, optimizing workflows, and enabling personalized medicine. This review explores the evolution of AI in medical imaging, from early rule-based systems to contemporary deep learning applications, while examining both ground-breaking innovations and persistent challenges. Key advancements include AI's superior performance in detecting tumours, fractures, and hemorrhages; workflow improvements through automated segmentation and prioritized case triage; and cutting-edge techniques like low-dose imaging reconstruction and radio genomics. However, significant barriers remain, including data quality issues, "black box" algorithm limitations, clinical adoption resistance, and ethical concerns regarding bias and privacy. The future of AI in radiology points toward explainable AI (XAI) for transparent decision-making, federated learning for privacy-preserving collaboration, and integration with emerging technologies like augmented reality. Successful implementation will require addressing technical, regulatory, and socioeconomic challenges while maintaining human oversight. As AI continues to transform medical imaging, its ultimate measure of success will be its ability to improve patient outcomes across diverse healthcare settings while upholding the highest standards of safety, equity, and clinical relevance.

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