Clinical validation of artificial intelligence for cancer detection prognosis and analysis across imaging modalities and tumor types

This study evaluates the diagnostic and prognostic performance of artificial intelligence in cancer detection across imaging modalities and tumor types. The review synthesizes current evidence on deep learning, radiomics, multimodal integration, and precision oncology frameworks, with emphasis on validation quality and clinical translation. A systematic review of 180 peer reviewed studies published between 2015 and 2025 was conducted. Included investigations assessed artificial intelligence applications in radiology, digital pathology, endoscopy, PET imaging, and multimodal platforms. Extracted data covered diagnostic accuracy, AUC, sensitivity, specificity, dataset size, validation strategy, and study design. Subdomains included detection, prognosis, grading, survival prediction, radio-genomics, explainability, federated learning, and regulatory evaluation. Across breast, lung, prostate, colorectal, brain, and skin cancers, multiple high-quality cohorts reported AUC values above 0.90. Whole slide histopathology achieved the highest discrimination, reaching AUC 0.99 in curated datasets. CT and MRI systems demonstrated strong performance with multicenter validation, including large scale lung and breast screening studies. Radiomics contributed to survival prediction, immunotherapy response modeling, and molecular subtype inference. Multimodal and transformer-based architectures improved representation learning in volumetric and cross domain data. Despite strong retrospective performance, only 21 percent of studies included external validation, 14 percent incorporated multicenter datasets, and 6 percent applied prospective or temporal validation. Artificial intelligence shows consistent high performance in cancer detection and prognostic modeling across imaging domains. Strongest evidence supports adjunctive use in screening, digital pathology, and endoscopic detection. Broader clinical adoption requires prospective trials, multicenter external validation, standardized reporting, explainability integration, and alignment with regulatory frameworks to ensure reliable deployment in precision oncology.