Artificial Intelligence (AI) in Cancer Radiology-Recent Cureus Review of Literature
A recent literature review outlined how artificial intelligence (AI) is redefining radiology as a systemic enabler of precision oncology. By focusing on precision imaging, early detection, integration, explainability, federated learning, clinical support, prognosis, equity, and workforce transformation, the impact of AI extends beyond technical innovation to systemic changes in healthcare.
Radiology is evolving into a predictive, collaborative, and ethically governed field central to precision oncology. AI success will be measured by its transparency, inclusivity, and effectiveness across health systems. AI must complement radiologists, who need to integrate imaging, data, and clinical judgment for patient-centred care.
This review was published in December 2025, in Cureus
Artificial Intelligence (AI) as a facilitator of Precision Radiology
Compared with traditional interpretation, which is affected by training and fatigue, AI analysis is more consistent in MRI and density reporting. It reduces inter-observer variability and enables multicentre research that requires comparable measures. AI-driven automated segmentation provides reproducible tumour measurements for accurate staging and monitoring. These volumetric markers can predict survival and serve as potential oncology endpoints.
Artificial Intelligence (AI) in early detection surpasses traditional screening methods
AI improves screening by detecting early changes and reducing false positives. AI enables personalised screening by combining imaging, demographic, and genomic data to generate risk scores. High-risk patients receive intensive monitoring, while low-risk groups avoid unnecessary imaging, reducing costs and overdiagnosis. As a triage tool in mass screening, AI-driven automated identification of low-risk studies reduces radiologists’ workload by 50% without compromising accuracy.
AI for image reconstruction and enhancement
AI is transforming image reconstruction by addressing conflicts among safety, efficiency, and diagnostic clarity. AI reconstruction algorithms produce high-quality images with lower radiation doses, reducing patient radiation exposure without compromising diagnostic accuracy. AI eliminates artefacts that interfere with interpretation, reducing motion artefacts and contrast variations that require repeat imaging, thereby saving costs and reducing radiation exposure. AI-driven super-resolution capability improves detail detection beyond hardware limits.
Combining AI with Clinical Decision Support Systems (CDSS)
The transformative potential of AI lies in its integration into a comprehensive CDSS. Treatment planning requires integrating imaging data with pathology, genomics, clinical records, and patient history.
AI-enabled CDSS platforms integrate these aspects, moving radiology towards multidisciplinary care. AI dashboards that combine imaging and clinical information support multidisciplinary tumour boards, enabling standardised, faster decision-making. AI achieves clinical significance when integrated into CDSS that unify imaging, molecular, and clinical data for precision oncology.
AI for prognosis and treatment monitoring
Using imperceptible imaging features, AI tools can predict disease progression and identify early markers of therapeutic response, making imaging more dynamic than static. Together, these capabilities illustrate how AI shifts cancer management from intermittent assessment to continuous, data-driven decision-making across the entire care pathway, reducing time spent on ineffective treatments and improving survival.
Possible Learnings for Stakeholders
AI is rapidly transforming oncological radiology, enabling earlier detection, greater precision, and treatment monitoring, thereby enhancing clinical decision-making and more personalised care. To prevent the exacerbation of disparities and to support sustainable implementation, federated learning, inclusive data representation, and clinician training are imperative. International standardisation, ethical governance, and shared validation frameworks are essential for AI to operate as an interoperable ecosystem that delivers equitable cancer care on a global scale.
Reference: Gurjar P, Mayana S, Reddy Annadevula S, et al. Artificial Intelligence in Radiology: Advancing Precision, Accuracy, and Early Detection in Cancer Diagnosis. Cureus 17(12): e100102. Published December 26, 2025. DOI 10.7759/cureus.100102
