"AI-driven tools have the potential to offer faster, more accurate, and less invasive methods to detect and diagnose cancer. As one example, using over 163,000 low-dose computed tomography (CT) scans of the chest and 49 types of clinical records—such as pathology test results and demographic information—researchers recently developed an AI model to enhance lung cancer diagnosis. The model automates the analysis of 3D CT scans and integrates this information with clinical data. It assists radiologists—doctors trained to create and interpret medical images such as X-rays and CT scans—in identifying lung nodules and assessing cancer risk (1035)Niu C, et al. (2025) Nat Commun, 16: 1523..\n\nResearchers have also developed a novel DL model to detect pancreatic tumors from CT images with over 94 percent accuracy, highlighting the potential for AI in identifying hard-to-detect cancers (1036)Mekala S, et al. (2025) Sci Rep, 15: 19787.. In another study, researchers using a DL model achieved more than 99 percent accuracy in diagnosing endometrial cancer from histopathologic images, showing how this approach can reduce diagnostic delays, catch cancer earlier, and improve patient outcomes (1037)Sheakh MA, et al. (2025) Computer Methods and Programs in Biomedicine Update, 7: 100181.."