(untitled) (The Review of Diabetic Studies, 2025)
Background Diagnostic imaging has evolved from Roentgen’s 1895 X-ray discovery to advanced hybrid modalities like PET-CT and AI-integrated systems, transforming non-invasive visualization of anatomy, physiology, and pathology. These innovations enhance diagnosis, treatment planning, and monitoring across oncology, cardiology, and neurology, supporting personalized medicine amid rising chronic disease burdens.​ Methods A systematic literature review searched PubMed, Scopus, Web of Science, and Embase from 1895 to November 2025 using terms like "diagnostic imaging evolution" and "AI in medical imaging." Over 5,000 articles were screened per PRISMA guidelines, yielding 450 peer-reviewed sources for qualitative synthesis, supplemented by grey literature from RSNA and ECR conferences.​ Results Key advancements include digital radiography, multi-detector CT, high-field MRI, 3D/4D ultrasound, and hybrid PET-MRI, enabling dose reductions up to 91%, superior soft-tissue contrast, and AI-driven lesion detection reducing false negatives by 30%. Integration with PACS, EHR, and radiomics improves workflows, early detection, and precision theranostics, though challenges persist in accessibility, workforce training, and AI ethics.​ Conclusions Diagnostic imaging innovations profoundly impact clinical outcomes by accelerating decisions and personalizing care, yet equitable access, regulatory harmonization, and bias mitigation are essential for future multimodal, AI-augmented, and sustainable applications in global health.​