Artificial intelligence is increasingly transforming diagnostic pathology, particularly in the field of breast pathology, according to a review published on June 26. The review outlines that while advancements are ongoing, many practicing pathologists are still unfamiliar with core AI concepts and their practical applications.
The article introduces foundational AI concepts such as algorithms, models, machine learning, deep learning, neural networks, and multimodal models to establish a shared understanding. It distinguishes between generative, black-box, and explainable AI approaches and emphasizes the importance of transparency and interpretability for clinical use.
The evolution of AI in breast pathology is traced from early rule-based computer-assisted diagnostic systems to current deep learning techniques utilizing large-scale whole-slide imaging datasets. Current clinical applications include detection of lymph node metastases, Nottingham grading of tumors, classification of benign versus malignant lesions, and automated quantification of biomarkers. The review also discusses how AI supports prognosis determination, risk stratification for patients, prediction of treatment response, and analysis of the tumor microenvironment.
Challenges associated with implementing these technologies in real-world settings are addressed in the review. These challenges include ensuring data quality, mitigating bias, addressing regulatory issues, costs involved with adoption, infrastructure requirements, and integration into existing workflows.
According to the article's authorship summary based on literature review and personal experience, "AI is transforming breast pathology by improving diagnostic accuracy, efficiency, and reproducibility across multiple applications... By reducing interobserver variability...and enhancing precision medicine...AI is becoming an indispensable partner to pathologists rather than a replacement for them." The integration of computational intelligence with human expertise has potential implications for advancing diagnosis methods as well as patient outcomes.
