Artificial Intelligence in Diagnostic Radiology: Current Applications, Clinical Impact, and Future Perspectives

Abstract

Artificial intelligence (AI) has emerged as a transformative technology in diagnostic radiology, offering innovative solutions for image interpretation, workflow optimization, and clinical decision-making. Recent advances in machine learning, deep learning, and related computational approaches have enabled the development of systems capable of analyzing complex imaging datasets with high accuracy and efficiency. AI applications are increasingly being integrated across multiple imaging modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, mammography, and positron emission tomography. In addition to disease detection and classification, AI supports image segmentation, radiomics, predictive analytics, image reconstruction, automated reporting, and quality assurance, thereby enhancing diagnostic performance and operational efficiency. Despite these advances, challenges related to data quality, algorithmic bias, generalizability, explainability, ethical considerations, and regulatory compliance continue to influence the clinical adoption of AI technologies. Emerging developments such as foundation models, generative artificial intelligence, large language models, multimodal learning, and privacy-preserving techniques are expected to further expand the role of AI in medical imaging. This review provides an overview of the fundamental concepts of AI in radiology, examines its current clinical applications, discusses key challenges and limitations, and explores future directions that may shape the next generation of diagnostic imaging. The continued integration of AI has the potential to improve diagnostic accuracy, streamline radiological workflows, and support more personalized patient care.