AI is transforming breast pathology by improving diagnostic accuracy, efficiency, and reproducibility. It is expected to become a complementary tool for pathologists rather than a replacement.
Summary
A review article published in the Journal of Clinical and Translational Pathology examines the applications, challenges, and future directions of artificial intelligence (AI) in breast pathology. The authors aim to provide an accessible overview of AI concepts and their clinical implications for practicing pathologists.
Methods
The review incorporated pertinent literature and personal experiences of the authors.
Key Concepts
- Key AI concepts covered include: algorithms, models, architectures, machine learning, deep learning, neural networks, and multimodal and foundational models.
- Distinctions are made between generative, black-box, and explainable AI, with a strong emphasis on transparency and interpretability.
Historical Evolution
AI in breast pathology evolved from early rule-based computer-assisted diagnostic systems to modern deep learning approaches using whole-slide imaging datasets.
Current Applications
AI applications now span a wide range of critical tasks:
- Detection of lymph node metastases
- Nottingham grading
- Classification of benign and malignant lesions
- Automated quantification of biomarkers
- Prognosis and risk stratification
- Prediction of treatment response
- Analysis of the tumor microenvironment
Challenges
Implementation challenges remain significant and include data quality, bias, regulatory issues, cost, infrastructure, and workflow integration.
Conclusions
The authors state that AI is transforming breast pathology by improving diagnostic accuracy, efficiency, and reproducibility. They note that AI is expected to become a complementary tool for pathologists rather than a replacement, with the potential to significantly advance breast cancer diagnosis and treatment.