Emerging Trends in Radiology: Integration of Artificial Intelligence with Modern Imaging Modalities for Enhanced Patient Care

Radiology has evolved significantly from conventional imaging techniques to advanced digital and multimodal diagnostic systems. The rapid growth in imaging data has increased the complexity of radiological interpretation, leading to challenges such as higher workload, longer reporting time, and risk of diagnostic errors. In this context, the integration of Artificial Intelligence (AI), particularly machine learning and deep learning, has emerged as a transformative approach in modern radiology. AI enhances various stages of the imaging workflow, including acquisition, reconstruction, interpretation, and reporting, thereby improving efficiency and accuracy. AI-driven technologies in imaging modalities such as CT, MRI, mammography, ultrasound, and PET scans have demonstrated significant improvements in image quality, noise reduction, and faster processing. These systems assist in early disease detection, automated segmentation, and precise quantification of pathological changes, enabling better clinical decision-making. Furthermore, AI contributes to personalised healthcare through radiomics and predictive analytics by integrating imaging data with clinical and genomic information. The findings indicate that AI integration reduces workflow time, enhances diagnostic performance, and improves patient outcomes. Despite these advantages, challenges such as data privacy, algorithm transparency, and potential bias must be addressed for effective implementation. Overall, AI is expected to play a crucial role in advancing radiology by supporting clinicians and enabling more accurate, efficient, and patient-centred healthcare systems.