Edge learning applications in the prediction and classification of combined hepatocellular-cholangiocarcinoma: A comprehensive narrative review

Core Tip

Core Tip: Edge learning presents a novel approach in combined hepatocellular-cholangiocarcinoma diagnosis and classification, leveraging decentralized artificial intelligence for real-time processing and enhanced data privacy. Unlike traditional cloud-based artificial intelligence, edge learning enables on-site analysis of histopathological features and medical imaging (computed tomography, magnetic resonance imaging) while reducing latency and bandwidth usage. This review explores its technical integration, including federated learning, deep learning optimizations (convolutional neural networks, pruning, quantization), and privacy-preserving artificial intelligence frameworks. By overcoming challenges like diagnostic complexity and data security, edge learning enhances clinical decision-making, treatment planning, and diagnostic accuracy, offering a transformative potential in precision oncology and liver cancer management.