Liver Cancer Detection, Prognostic Modeling, and Treatment Response Prediction using AI

Abstract

Artificial intelligence (AI) is transforming the treatment of liver cancer by improving clinical decision-making, improving prognostic modeling, and boosting early detection. By using deep learning methods, big data analytics, and sophisticated machine learning algorithms, AI systems are able to examine complicated datasets, including genomic profiles, imaging modalities (e.g., MRI, CT), and electronic health records, to find minute patterns that could be signs of hepatocellular carcinoma (HCC) and other hepatic cancers in their early stages.

AI-driven technologies perform better in early detection than conventional diagnostic techniques by increasing sensitivity and specificity, which makes prompt intervention possible. AI combines clinical factors and multi-omics data for prognostic modeling, allowing for the development of tailored treatment plans by predicting disease progression, response to treatment, and survival outcomes with previously unheard-of accuracy.

 Additionally, by offering evidence-based suggestions, lowering diagnostic uncertainty, and expediting interdisciplinary workflows, AI supports clinical decision-making. Notwithstanding these developments, issues including model interpretability, data heterogeneity, and ethical implications still exist.

This abstract highlights the revolutionary potential of AI in the treatment of liver cancer, but it also emphasizes the necessity of thorough validation and clinical practice integration to fully reap its benefits.

Keywords- AI, Liver Cancer, Early Detection, Prognostic Modeling,