On the Implementation of AI systems in Oncology

Traditional cancer diagnosis is often hindered by subjective interpretation, high costs, and significant false-negative rates, which can delay vital early-stage detection and reduce patient survival rates. To address these limitations, Artificial Intelligence (AI) and Deep Learning (DL) frameworks have emerged as transformative tools in modern oncology. This review evaluates the application of DL and Convolutional Neural Networks (CNNs) in medical imaging, specifically focusing on Computer-Aided Diagnosis (CAD), automated image segmentation, and lesion classification across lung, breast, brain, cervical, and liver cancers. While these AI systems demonstrate diagnostic accuracy and precision comparable to professional radiologists, their clinical implementation is currently obstructed by the “unexplainability” of complex algorithms, the scarcity of high-quality training datasets, and profound ethical concerns regarding patient privacy and data consent. Furthermore, this paper explores the critical role of Explainable AI (XAI), including techniques like SHAP, LIME, and Grad-CAM, in bridging the accuracy-explainability tradeoff by making AI reasoning transparent. Ultimately, fostering clinical trust through transparent reasoning and ethically sourced data is essential for the future integration of AI in daily diagnostic workflows.