Artificial intelligence in hepatocellular carcinoma screening: applications and challenges

TL;DR

This paper reviews how artificial intelligence can improve early detection of liver cancer but highlights challenges that need to be addressed for its full use in clinics.

Contribution

A critical evaluation of AI applications and challenges in hepatocellular carcinoma screening.

Findings

AI improves early detection of HCC by analyzing clinical data more effectively than traditional methods.

Challenges like ethical issues and model instability hinder AI's widespread clinical use for HCC screening.

Recent studies show AI's potential to enhance patient outcomes through better early diagnosis.

Abstract

Hepatocellular carcinoma (HCC) is the predominant histological subtype of primary liver cancer, with a 5-year survival rate of approximately 18%. Early detection of HCC is critical for guiding treatment selection and improving patient survival outcomes. The effectiveness of conventional screening methods is decreased due to their inherent limitations and individual variability. Artificial intelligence (AI) has advanced rapidly in medical practice and has played a significant role in increasing the early detection rates of HCC by replacing manual tasks and accessing hidden information in routinely available clinical data. However, numerous challenges, such as ethical concerns, model instability, and generalizability, must be overcome before their full clinical implementation. This article reviews recent studies that describe AI-based models for the early diagnosis of HCC, focuses on the current applications and persistent challenges of AI in HCC screening and discusses its perspectives. We aim to provide a critical evaluation of the potential of AI for enhancing early HCC detection and improving patient prognosis.

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