Artificial intelligence in cancer pathology: Applications, challenges, and future directions

Area of Science:

• Digital pathology
• Computational oncology
• Medical artificial intelligence

Background:

• Artificial intelligence (AI) demonstrates significant potential in enhancing cancer pathology.
• AI applications span diagnostics, workflow optimization, and precision oncology.

Purpose of the Study:

• To review current AI applications in cancer pathology across diverse cancer types.
• To identify key AI technologies and their roles in cancer diagnosis and prognosis.
• To explore challenges and future directions for AI in cancer pathology.

Main Methods:

• Review of current literature on AI applications in breast, lung, prostate, and colorectal cancer pathology.
• Analysis of machine learning, deep learning, and computer vision in histopathological image analysis.
• Examination of multi-modal data integration for enhanced diagnostic capabilities.

Main Results:

• AI aids in accurate tissue classification, mutation detection, and prognostic predictions.
• Machine learning, deep learning, and computer vision are pivotal AI technologies.
• Significant progress has been made in automated analysis of histopathological images.

Conclusions:

• AI offers substantial promise for improving cancer pathology and personalized oncology.
• Key challenges include data privacy, model interpretability, and regulatory compliance.
• Future directions involve real-time diagnostics, explainable AI, and global accessibility through AI-pathologist collaboration.