The increasing incidence of tumors underscores the urgent need for precise diagnostic tools to enhance treatment outcomes and improve patient prognosis. Traditional diagnostic methods, often limited by subjectivity and variability, struggle to meet the demands of modern oncology. This study aims to construct an artificial intelligence (AI)-assisted tumor precision diagnosis model and explore its clinical application value. We collected comprehensive multicenter tumor imaging and clinical data, including histopathological features and patient demographics. Using advanced deep learning algorithms, we developed a diagnostic model capable of distinguishing various tumor types with high accuracy. The model was rigorously validated on an independent dataset, demonstrating superior performance compared to traditional diagnostic methods in terms of diagnostic accuracy, sensitivity, and specificity. For example, in ultrasonographic detection of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), the AI model showed a significant improvement in diagnostic sensitivity. Additionally, the model exhibited good generalizability across different tumor types and clinical settings, indicating its potential for widespread application. The conclusion indicates that the AI-assisted diagnostic model can significantly enhance the precision of tumor diagnosis, providing strong support for clinical decision-making and holding important application prospects. Future research will focus on further optimizing the model architecture and expanding its clinical applications to cover a broader range of tumor types and clinical scenarios. The integration of AI into clinical practice holds promise for improving diagnostic efficiency and patient outcomes in oncology, ultimately contributing to the development of precision medicine.