Artificial Intelligence in Radiology: Advancing Precision, Accuracy, and Early Detection in Cancer Diagnosis

Abstract

Artificial intelligence (AI) is rapidly transforming oncologic radiology, enabling earlier detection, greater precision, and more personalized care. Yet much of the literature remains fragmented into disease-specific studies or narrow performance assessments. This review addresses that gap through a narrative thematic synthesis of research published between 2019 and 2025, identified from major biomedical and engineering databases and selected for clinical relevance, translational value, and policy significance. Unlike prior reviews that catalog isolated applications, it organizes evidence into cross-cutting frameworks that redefine radiology's role in cancer care. These include advances in precision imaging and early detection, the integration of multimodal data for richer disease characterization, and the use of AI in prognosis and treatment monitoring. Equally, the review highlights challenges of model explainability, federated learning, equity, and workforce adaptation as determinants of adoption. By situating these themes within Clinical Decision Support Systems (CDSS) and broader healthcare infrastructures, the analysis shows that AI's significance lies less in isolated accuracy gains than in its transparency, inclusivity, and adaptability across contexts. The review concludes that the decisive priority now is to build global collaborations, robust validation, and ethical frameworks that ensure AI evolves as an inclusive ecosystem capable of delivering equitable improvements in cancer care worldwide.

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