An Artificial Intelligence Pipeline for Hepatocellular Carcinoma: From Data to Treatment Recommendations
Xuebing Zhang, Liuxin Yang, Chengxiang Liu, Xingxing Yuan, Yali Zhang
Abstract
Hepatocellular carcinoma (HCC) poses significant clinical challenges, including difficulties in early diagnosis and the complexity of treatment options. Artificial intelligence (AI) technologies are emerging as powerful tools to address these issues through a unified AI pipeline. This pipeline begins with data ingestion and preprocessing, integrating multimodal data such as imaging, genomic and clinical records. Machine learning and deep learning techniques are then applied to analyze these data, improving tumor detection, characterization, and early diagnosis. The pipeline extends to personalized treatment planning, where AI integrates diverse data types to predict patient responses to various therapies. In drug development, AI accelerates the discovery of new treatments through virtual screening and molecular modeling, while also identifying potential new uses for existing drugs. AI further enhances patient management through remote monitoring and intelligent support systems, enabling real-time data analysis and personalized care. In research, AI improves big data analysis and clinical trial design, uncovering new biomarkers and optimizing patient recruitment and outcome prediction. However, challenges such as data quality, standardization, and privacy remain. Future developments in multimodal data integration and edge computing promise to further enhance AI's impact on HCC diagnosis, treatment, and research, leading to improved patient outcomes and more effective management strategies.