Application of artificial intelligence in medical imaging has transformed patient-specific diagnosis and disease management processes. AI-imaging, which consists of sophisticated algorithms such as machine learning, deep learning, and natural language processing, increases accuracy in detecting diseases, segmentation, classification, and outcome prediction. Application of AI in imaging technologies such as MRI, CT scans, ultrasound, and PET scans improves diagnostic accuracy, operational effectiveness, and decision-making. AI-assisted radiomics measures imaging biomarkers to perform non-invasive diagnosis and treatment planning. The combination of AI with genomics and multi-omics information is revolutionary to create extremely personalized disease models and therapeutic interventions. This book chapter explores the real-time applications of AI in neuroimaging, cardiac, chest, breast, and musculoskeletal imaging are also considered. AI deployment in personalized imaging is, however, hindered by algorithmic bias, data privacy issues, clinical mistrust, and regulatory challenges. Methods to overcome these obstacles are XAI, standardized procedures, and collaborative design processes. AI-based imaging is a key step towards precision diagnosis and personalized care, set to revolutionize the future of modern medicine.