ARTIFICIAL INTELLIGENCE IN DIAGNOSTIC RADIOLOGY

Abstract

Artificial intelligence (AI) has emerged as a transformative force in diagnostic radiology, fundamentally reshaping medical imaging through enhanced automation, superior image analysis, streamlined processes, and advanced predictive analytics. The incorporation of machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) into radiological practice has substantially improved diagnostic precision, reduced reporting latency, and enabled earlier disease identification. AI-driven technologies are progressively deployed across modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Mammography, and conventional X-ray imaging, augmenting radiologists in detecting pathological findings with greater accuracy and consistency. Beyond detection, AI contributes to dose optimization, advanced image reconstruction, and evidence-based clinical decision-making, thereby improving patient safety and healthcare system efficiency. Despite these benefits, integration challenges persist, encompassing ethical considerations, data protection obligations, algorithmic bias, medicolegal accountability, and the demand for robust regulatory frameworks. This chapter comprehensively examines the foundational principles, clinical applications, documented benefits, implementation obstacles, and prospective directions of AI-driven methodologies in diagnostic radiology, underscoring its increasingly indispensable role in contemporary medical imaging practice.