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

Abstract

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare heterogeneous primary malignant liver tumor containing both hepatocellular and cholangiocarcinoma features. The complex presentation of cHCC-CCA tends to be poorly investigated, and the information derived from traditional diagnostic techniques (histopathology and radiological imaging) is often not optimal. Since cHCC-CCA is usually difficult to diagnose due to complex histopathological features (edge learning) as excessive photos, hence, achieves treatment delays and poor prognosis, the incorporation of advanced artificial intelligence like edge learning is able to improve the patient’s outcome. Using artificial intelligence, particularly deep learning, has recently opened new doorways for the improvement of diagnostic accuracy. If artificial intelligence models are deployed on local devices, edge learning exercises this type of learning, which provides real time processing, improved data privacy and reduced bandwidth usage. This narrative review investigates the conceptual formulation of edge learning together with its opportunities for clinical applications in the prediction and classification of cHCC-CCA, the technical solution strategies, the clinical benefits it offers, and associated challenges and future directions.

Keywords: Edge learning; Combined hepatocellular-cholangiocarcinoma; Artificial intelligence; Disease prediction; Diagnostic accuracy

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.