Applications and challenges of artificial intelligence in cancer management

Artificial intelligence (AI) is increasingly transforming cancer management by enabling the analysis of complex multimodal data generated across the cancer care continuum. This structured narrative review synthesizes current applications of AI, machine learning, and deep learning in cancer detection, imaging diagnosis, tumor characterization, histopathology, genomics, drug discovery, precision oncology, prognosis, treatment-response prediction, clinical decision support, adherence monitoring, and supportive care. A literature search was conducted using PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for publications from July 2001 to November 2025. From 564 identified records, 234 articles were included in the final thematic synthesis after duplicate removal, title/abstract screening, and full-text assessment. The reviewed evidence shows that AI models, including convolutional neural networks, U-Net-based segmentation models, radiomics approaches, hybrid models, multiple-instance learning, federated learning, large language models, and multimodal frameworks, have demonstrated substantial potential in extracting clinically meaningful patterns from imaging, pathology, genomic, biomarker, and clinical data. These tools may support earlier detection, improved tumor classification, non-invasive molecular profiling, treatment optimization, and personalized decision-making. However, clinical readiness remains uneven, as many models are limited by retrospective designs, dataset heterogeneity, data leakage, overfitting, limited explainability, insufficient external validation, and uncertain generalizability across global healthcare settings. Future progress will require diverse multicenter datasets, standardized annotation, transparent reporting, explainable and hybrid modelling, ethical data governance, regulatory oversight, and post-deployment monitoring. AI should therefore be viewed as clinician-supervised decision support that can strengthen precision oncology when implemented responsibly.