The integration of artificial intelligence (AI) in radiology has opened new possibilities for optimizing diagnostic workflows, enhancing image quality, and ensuring patient safety. One emerging area of innovation is the use of AI in the management of contrast media. Contrast-enhanced imaging procedures are essential in modern radiological practice, yet they come with challenges such as individualized dosing, risk of adverse reactions, and protocol standardization. AI-driven tools now offer promising solutions for personalizing contrast protocols, reducing unnecessary exposure, and predicting patient-specific risks. These developments not only improve diagnostic accuracy but also align with the broader goals of precision medicine. However, this field is still evolving, and further research is needed to validate and integrate these innovations into routine clinical practice.

This Research Topic aims to explore how AI can revolutionize contrast media use in radiological imaging by improving safety, efficiency, and diagnostic precision. Contrast agents are indispensable in imaging, but their use is associated with risks such as nephrotoxicity, hypersensitivity reactions, and variability in dosing. AI-driven systems offer potential solutions through predictive modeling, personalized contrast administration, and protocol optimization based on individual patient characteristics. Moreover, the integration of radiomics—the extraction of high-dimensional imaging features from contrast-enhanced scans—represents a powerful extension of this approach. AI can facilitate advanced radiomic analysis, enabling non-invasive assessment of disease phenotypes, prognosis, and treatment response. These capabilities promise to elevate the diagnostic and prognostic utility of contrast-enhanced imaging beyond conventional interpretations. This Research Topic seeks to collect high-quality evidence, novel methodologies, and practical insights on the application of AI and radiomics in contrast media management, promoting innovation and clinical translation.

We welcome interdisciplinary contributions that explore the role of AI and radiomics in contrast media management across all imaging modalities (e.g., CT, MRI, angiography). Relevant themes include:

-AI-driven contrast dose optimization and administration strategies

-Risk prediction models for contrast-induced acute kidney injury and allergic reactions

-Decision-support systems for contrast protocol personalization

-Radiomic analyses from contrast-enhanced studies and their clinical implications

-Integration of AI into radiology workflows involving contrast agents

We encourage the submission of original research, systematic reviews, meta-analyses, brief research reports, case studies, and perspectives. Contributions that demonstrate clinical applicability, technical innovation, or translational potential are particularly welcome.

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AI-Driven Innovations in Contrast Media: From Protocol Optimization to Risk Reduction