AI-Driven Approaches for Improved Detection and Diagnosis of Pancreatic Cancer

Pancreatic Cancer, although one of the most dangerous cancers, has an unfavorable prognosis because of the fact that there are no particular symptoms and it is usually diagnosed on a later stage. This review discusses the possibilities offered by artificial intelligence and especially machine learning and deep learning techniques for the enhancement of detecting and diagnosing pancreatic cancer. The review emphasizes advanced AI models for pancreatic tumor’s classification and outlining which include convolutional neural networks (CNNs) as well as segmentation methods. These AI enhanced approaches aim at achieving accurate, early diagnosis by integrating multiple image modalities including contrast-enhanced computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS) and histopathological images as well as biomarkers CA19-9 and genetic information. Besides, the paper analyses some optimization methods that ensure model performance and data security in a cooperative multi-institutional environment, such as federated learning and genetic algorithms. It is also important to consider how intelligence can reduce misdiagnosis, reduce the number of clinics, and save money for patients. This article addresses the challenges in image analysis, provides a comprehensive review of AI applications in cancer diagnosis, and offers suggestions for future research on how to create AI systems that can be easily integrated.