Artificial Intelligence in Radiology: A Survey on Transforming Diagnostic Accuracy and Clinical Decision-Making

Artificial Intelligence (AI) has emerged as a transformative force in modern radiology, driven by rapid advances in machine learning (ML) and deep learning (DL) techniques. As radiology is a data-intensive specialty, the increasing volume and complexity of medical imaging have created a growing demand for intelligent tools that can enhance diagnostic accuracy, efficiency, and clinical decision-making. This survey-based review aims to evaluate and assess the current role of AI in radiology and its impact on diagnostic performance and clinical practice. This study systematically reviews and synthesizes peer-reviewed literature published between 2019 and 2025, focusing on AI applications across major imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-ray, mammography, ultrasound, and positron emission tomography (PET). Relevant studies were identified through major academic databases, and the findings were analyzed narratively to assess improvements in diagnostic accuracy, workflow optimization, and decision-support capabilities. Particular attention was given to commonly used AI algorithms, such as convolutional neural networks (CNNs), ResNet, DenseNet, transformer-based models, and radiomics-driven machine learning approaches. The reviewed evidence demonstrates that AI-assisted radiology systems consistently achieve high levels of diagnostic accuracy, sensitivity, and specificity, in many cases comparable to or exceeding those of expert radiologists. AI tools also contribute to reduced reporting times, improved interobserver consistency, and enhanced prioritization of urgent cases. Furthermore, the integration of AI into clinical decision support systems enables predictive analytics that support personalized treatment planning and disease monitoring. Despite these benefits, this survey highlights several challenges that limit widespread clinical adoption, including data heterogeneity, limited external validation, algorithmic bias, lack of transparency, and evolving ethical and regulatory frameworks. In conclusion, AI represents a powerful complementary tool that enhances, rather than replaces, the role of radiologists. Continued interdisciplinary collaboration, rigorous validation, and responsible governance are essential to ensure the safe and effective integration of AI into radiological practice.