<![CDATA[Modern radiology may be classified as a pivotal determinant in the early detection of chronic diseases, particularly through advances in imaging technology and the integration of artificial intelligence (AI). This study aims to examine the contribution of CT, MRI, ultrasound, and PET to identifying subclinical abnormalities and to evaluate the impact of AI in enhancing diagnostic accuracy and operational efficiency. Employing a systematic and PRISMA-based scoping review methodology, data were collected from PubMed, Embase, Scopus, and IEEE Xplore (2015–2025). The analysis indicates that AI-assisted mammography achieved an AUC of 0.92 with 95% sensitivity and 78% specificity, while low-dose CT screening reduced lung cancer mortality by up to 20%. MRI- and ultrasound-based radiomics improved non-invasive detection of hepatic and renal fibrosis with >85% accuracy. Molecular PET and multimodal imaging demonstrated potential for detecting inflammatory and metabolic alterations at an early stage. Furthermore, AI improved interpretation efficiency by approximately 30% and reduced diagnostic errors by 25%. Nonetheless, data bias, limited external validation, and ethical issues in data governance remain persistent challenges. These findings affirm the strategic role of modern radiology as the cornerstone of preventive medicine and underscore the necessity for multidisciplinary collaboration to develop a standardized, ethically governed multimodal data ecosystem.]]>
