Artificial intelligence in breast pathology: Overview and recent updates.

Area of Science:

• Pathology
• Digital Pathology
• Artificial Intelligence

Background:

• Breast cancer diagnosis relies heavily on histopathology, facing challenges from workload, variability, and complexity.
• Digital pathology and whole slide imaging (WSI) pave the way for AI integration.
• AI offers solutions to enhance accuracy and efficiency in breast pathology.

Purpose of the Study:

• To review advancements in AI applications for breast pathology.
• To explore AI's role in diagnosis, grading, metastasis detection, and biomarker quantification.
• To discuss emerging AI applications and future directions in the field.

Main Methods:

• Review of current literature on AI in breast pathology.
• Analysis of AI applications in diagnosis, classification, grading, and biomarker analysis.
• Discussion of AI's potential in prognosis, treatment response, and biomarker discovery.

Main Results:

• AI shows significant progress in diagnosis, classification, grading, and biomarker quantification (ER, PR, HER2, Ki-67).
• Emerging AI roles include prognosis prediction, treatment response assessment, and tumor microenvironment analysis.
• Barriers to AI adoption include data quality, generalizability, interpretability, and regulatory hurdles.

Conclusions:

• AI is transforming breast pathology, offering improved accuracy and efficiency.
• Addressing challenges like data quality and clinical integration is crucial for widespread AI adoption.
• Future research should focus on foundation models, multimodal data, explainable AI, and real-world validation.