Artificial intelligence improves diagnostic accuracy in clinical breast pathology

Background and objectives

Artificial intelligence (AI) is increasingly reshaping diagnostic pathology, with breast pathology representing one of the most advanced and clinically impactful areas of adoption. Despite rapid progress, many practicing pathologists remain unfamiliar with core AI concepts and their practical implications. This review provides a concise and accessible overview of AI in breast pathology, focusing on foundational principles, current clinical applications, and future directions.

Methods

Pertinent literature was reviewed. Personal experiences were also summarized and incorporated.

Results

Key AI concepts, including algorithms, models, architectures, machine learning, deep learning, neural networks, and multimodal and foundational models, are introduced to establish a common framework. Important distinctions among generative, black-box, and explainable AI are highlighted, emphasizing the need for transparency and interpretability in clinical settings. The evolution of AI in breast pathology is reviewed, from early rule-based computer-assisted diagnostic systems to modern deep learning approaches leveraging large-scale whole-slide imaging datasets. Current applications span multiple domains, including detection of lymph node metastases, Nottingham grading, classification of benign and malignant lesions, and automated quantification of critical biomarkers. AI-based approaches to prognosis, risk stratification, prediction of treatment response, and analysis of the tumor microenvironment are also discussed. Finally, the review addresses challenges associated with real-world implementation, including data quality, bias, regulatory considerations, cost, infrastructure, and workflow integration.

Conclusions

AI is transforming breast pathology by improving diagnostic accuracy, efficiency, and reproducibility across multiple applications, including tumor detection, Nottingham grading, biomarker quantification, risk stratification, and prognostic prediction. The field has rapidly evolved from early rule-based approaches to sophisticated deep learning and multimodal foundation models capable of comprehensive disease characterization and supporting increasingly personalized treatment strategies. By reducing interobserver variability, streamlining workflows, and enhancing precision medicine, AI is becoming an indispensable partner to pathologists rather than a replacement for them. Ultimately, the integration of computational intelligence with human expertise has the potential to significantly advance breast cancer diagnosis, treatment, and patient outcomes.