Artificial intelligence in hepatocellular carcinoma screening: applications and challenges

Area of Science:

• Medical Imaging
• Oncology
• Artificial Intelligence

Background:

• Hepatocellular carcinoma (HCC) is a leading cause of cancer death with poor survival rates.
• Early detection of HCC is crucial for effective treatment and improved patient outcomes.
• Conventional screening methods for HCC have limitations and variability.

Purpose of the Study:

• To review AI models for early HCC diagnosis.
• To discuss current applications and challenges of AI in HCC screening.
• To evaluate AI's potential in enhancing early HCC detection and patient prognosis.

Main Methods:

• Review of recent studies on AI-based models for HCC early diagnosis.
• Focus on AI applications, challenges, and perspectives in HCC screening.
• Critical evaluation of AI's role in enhancing early HCC detection.

Main Results:

• AI models are increasingly used for early HCC detection, leveraging clinical data.
• AI has the potential to overcome limitations of conventional screening methods.
• Significant challenges remain, including ethical concerns, model stability, and generalizability.

Conclusions:

• AI offers a promising approach to enhance early HCC detection and improve patient prognosis.
• Addressing ethical and technical challenges is vital for AI's clinical implementation in HCC screening.
• Further research and validation are needed to fully realize AI's potential in liver cancer diagnosis.