Advancing Early Cancer Detection through Secure Cloud Data Management and Artificial Intelligence

Abstract

The rapid convergence of artificial intelligence (AI) and cloud computing is reshaping the landscape of modern oncology. As the global burden of cancer intensifies, healthcare systems are under growing pressure to improve early detection, personalization of treatment, and scalability of diagnostic infrastructure. This study investigates the combined effect of AI-powered cancer detection models and cloud-based data management systems on diagnostic precision and preventive care outcomes. Drawing on multi-source clinical and genomic datasets, including electronic health records (EHRs), wearable device streams, and cancer registry data, this research employs a hybrid framework integrating convolutional neural networks (CNNs), ensemble learning, and gradient-boosted models deployed within secure cloud environments. Evaluation metrics such as AUC-ROC, sensitivity, specificity, and F1-score were used to assess performance across multiple cancer types, with a particular focus on early-stage esophageal and breast cancer. Our findings show a marked improvement in diagnostic precision (AUC ≥ 0.93) when clinical, genomic, and behavioral data are harmonized in a cloud-based architecture. The integration also significantly reduced latency in model inference, with real-time risk prediction capabilities enabling timely clinical intervention. Moreover, cloud-enabled interoperability across healthcare institutions enhanced patient tracking and treatment continuity, especially in under-resourced environments. This study establishes that the synergy between AI and cloud data management is not merely technical, it is transformative. It enables a shift from reactive treatment to proactive prevention, aligning healthcare delivery with the demands of precision medicine in the AI age.