A recent Cureus review article synthesizes the expanding role of artificial intelligence (AI) in oncologic radiology and cancer imaging, showing how machine learning, deep learning, radiomics, and related technologies are reshaping early detection, diagnosis, and personalised decision-making in cancer care. These AI systems are increasingly applied across multiple cancer types — including breast, lung, prostate, and brain cancers — and use a range of imaging modalities like CT, MRI, PET/CT, and digital mammography to support clinicians with complex image interpretation and quantitative tumor analysis.
The review highlights that AI models can match or exceed expert radiologists’ performance in many diagnostic tasks by detecting subtle patterns in imaging data that human readers might miss. For example, deep learning-based systems and radiomic frameworks have demonstrated high sensitivity and specificity in lesion detection and characterization, helping flag suspicious findings earlier than standard readings in some studies. This improvement has potential to speed up workflows, reduce diagnostic errors, and allow clinicians to prioritise complex cases.
Beyond detection, AI in radiology also contributes to prognostic modelling and personalized care planning — integrating imaging features with clinical and molecular data (radiogenomics) to help predict disease progression, treatment response, and patient outcomes. This multimodal integration could eventually enable more tailored therapy decisions and risk stratification, moving radiology from descriptive interpretation toward predictive, data-driven oncology.
Despite these advances, the review stresses persistent challenges before widespread clinical adoption: generalizability of algorithms across diverse populations, data quality and bias, ethical and regulatory concerns, explainability of AI outputs, and integration into clinical workflows. Addressing these issues — including through multicenter validation studies, clinician training, and transparent AI tools — will be essential for AI to fulfil its promise of enhancing cancer detection and outcomes without compromising safety or equity.
