The article "Exploring the Application and Future Outlook of Artificial Intelligence in Pancreatic Cancer" by Guohua Zhao, Xi Chen, Mengying Zhu, Yang Liu, and Yue Wang, published in *Frontiers in Oncology*, reviews the current state and future potential of AI in pancreatic cancer management. Pancreatic cancer is a highly invasive and deadly form of cancer, with limited early detection methods and high recurrence rates. The authors highlight the importance of early screening, diagnosis, and personalized treatment strategies. AI, particularly deep learning, has shown promise in improving diagnostic accuracy and predicting outcomes. Deep learning models have been used to develop predictive models for early detection, such as identifying precancerous lesions and high-risk subgroups within diabetic patients. These models have demonstrated high sensitivity and specificity, outperforming traditional methods. In the realm of diagnosis, AI-assisted imaging techniques, including CT and MRI, have enhanced the accuracy of tumor detection and staging. Deep learning algorithms have been trained to distinguish between benign and malignant lesions, improving diagnostic efficiency and precision. Endoscopic ultrasound (EUS) images have also benefited from AI, with models capable of differentiating between various conditions and improving surgical planning. AI has played a crucial role in surgical interventions, particularly in neoadjuvant therapy and postoperative complications. Models have been developed to predict the response to neoadjuvant therapy, guiding treatment decisions. Additionally, AI-driven tools have improved surgical navigation and precision, reducing complications and improving patient outcomes. The article also discusses the use of AI in predicting postoperative complications, such as pancreatic fistula, and in assessing prognosis, including recurrence risk. These applications have the potential to enhance patient care and improve survival rates. Despite the significant advancements, the authors acknowledge challenges, such as the interpretability of deep learning models and the need for standardized frameworks. Overall, the integration of AI in pancreatic cancer management holds great promise for improving diagnostic accuracy, treatment effectiveness, and patient outcomes.The article "Exploring the Application and Future Outlook of Artificial Intelligence in Pancreatic Cancer" by Guohua Zhao, Xi Chen, Mengying Zhu, Yang Liu, and Yue Wang, published in *Frontiers in Oncology*, reviews the current state and future potential of AI in pancreatic cancer management. Pancreatic cancer is a highly invasive and deadly form of cancer, with limited early detection methods and high recurrence rates. The authors highlight the importance of early screening, diagnosis, and personalized treatment strategies. AI, particularly deep learning, has shown promise in improving diagnostic accuracy and predicting outcomes. Deep learning models have been used to develop predictive models for early detection, such as identifying precancerous lesions and high-risk subgroups within diabetic patients. These models have demonstrated high sensitivity and specificity, outperforming traditional methods. In the realm of diagnosis, AI-assisted imaging techniques, including CT and MRI, have enhanced the accuracy of tumor detection and staging. Deep learning algorithms have been trained to distinguish between benign and malignant lesions, improving diagnostic efficiency and precision. Endoscopic ultrasound (EUS) images have also benefited from AI, with models capable of differentiating between various conditions and improving surgical planning. AI has played a crucial role in surgical interventions, particularly in neoadjuvant therapy and postoperative complications. Models have been developed to predict the response to neoadjuvant therapy, guiding treatment decisions. Additionally, AI-driven tools have improved surgical navigation and precision, reducing complications and improving patient outcomes. The article also discusses the use of AI in predicting postoperative complications, such as pancreatic fistula, and in assessing prognosis, including recurrence risk. These applications have the potential to enhance patient care and improve survival rates. Despite the significant advancements, the authors acknowledge challenges, such as the interpretability of deep learning models and the need for standardized frameworks. Overall, the integration of AI in pancreatic cancer management holds great promise for improving diagnostic accuracy, treatment effectiveness, and patient outcomes.