Background and purpose
Artificial intelligence (AI) is increasingly reshaping diagnostic pathology, and breast pathology is one of the most advanced and clinically impactful areas of implementation. Despite rapid advances, many practicing pathologists are still unfamiliar with the core concepts of AI and its practical implications. This review provides a concise and accessible overview of AI in breast pathology, focusing on basic principles, current clinical applications, and future directions.
method
We reviewed the relevant literature. I have also included a collection of personal experiences.
result
Key AI concepts such as algorithms, models, architectures, machine learning, deep learning, neural networks, multimodal and foundational models are introduced to establish a common framework. Important differences between generative AI, black box AI, and explainable AI are highlighted, highlighting the need for transparency and interpretability in clinical practice. We review the evolution of AI in breast pathology, from early rule-based computer-aided diagnostic systems to modern deep learning approaches that leverage large whole-slide image datasets. Current applications span multiple areas, including detection of lymph node metastases, Nottingham grading, classification of benign and malignant lesions, and automatic quantification of important biomarkers. AI-based approaches to prognosis, risk stratification, prediction of treatment response, and analysis of the tumor microenvironment are also discussed. Finally, this review addresses challenges related to real-world implementation, including data quality, bias, regulatory considerations, cost, infrastructure, and workflow integration.
conclusion
AI is transforming breast pathology by improving diagnostic accuracy, efficiency, and reproducibility across multiple applications, including tumor detection, Nottingham Classification, biomarker quantification, risk stratification, and prognosis prediction. The field has rapidly evolved from early rule-based approaches to sophisticated deep learning and multimodal-based models capable of comprehensive disease characterization and support of increasingly personalized treatment strategies. By reducing interobserver variability, streamlining workflows, and enhancing precision medicine, AI is becoming an indispensable partner for pathologists rather than a replacement for them. Ultimately, integrating computer intelligence and human expertise has the potential to significantly advance breast cancer diagnosis, treatment, and patient outcomes.
sauce:
Reference magazines:
Hu, Y. others. (2026). Artificial intelligence in breast pathology: recent advances in multimodal models, explainability, and clinical applications. Journal of Clinical Pathology. DOI: 10.14218/JCTP.2026.00007. https://www.xiahepublishing.com/2771-165X/JCTP-2026-00007
