Journal of Clinical Pathology and Laboratory Medicine

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Editorial - Journal of Clinical Pathology and Laboratory Medicine (2025) Volume 7, Issue 3

Application of artificial intelligence in anatomical pathology for cancer detection.

Sam Sadigh*

Department of Pathology, Brigham and Women’s Hospital,USA

*Corresponding Author:
Sam Sadigh
Department of Pathology
Brigham and Women’s Hospital,USA
E-mail: ssadig@bwh.harvard.edu

Received: 10-Sep-2025, Manuscript No. AACPLM- 25-166908; Editor assigned: 11-Sep-2025, Pre QC No. AACPLM- 25-166908 (PQ); Reviewed: 20-Sep- 2025, QC No. AACPLM- 25-166908; Revised: 21-Sep-2025, Manuscript No. AACPLM- 25-166908 (R); Published: 28-Sep-2025, DOI: 10.35841/ aacplm-7.3.269

Citation: Sadigh S. Application of artificial intelligence in anatomical pathology for cancer detection. J Clin Path Lab Med.2025;(7)3:269

Introduction

Artificial Intelligence (AI) has become a transformative force in modern medicine, particularly in anatomical pathology, where it significantly enhances cancer detection. Anatomical pathology, which involves the microscopic examination of tissue samples to diagnose diseases, is critical for identifying cancer types, stages, and prognoses. However, the manual analysis of histopathological slides is labor-intensive and prone to inter-observer variability. AI, especially deep learning and machine learning algorithms, is being increasingly integrated to improve diagnostic accuracy, efficiency, and reproducibility. [1].

One of the primary applications of AI in anatomical pathology is in digital image analysis. With the digitization of histopathological slides using whole-slide imaging (WSI), AI models can now analyze tissue architecture, cell morphology, and staining patterns to identify malignant features. Convolutional Neural Networks (CNNs), a class of deep learning models, have shown excellent performance in detecting various cancers such as breast, prostate, lung, and colorectal cancer by recognizing subtle patterns in tissue that may be overlooked by human eyes.[2].

AI also aids in tumor grading and subtyping. For instance, in prostate cancer, AI systems can distinguish between Gleason grades with high precision, leading to more consistent evaluations. Similarly, in breast cancer, AI models have been developed to classify ductal carcinoma in situ (DCIS) and invasive carcinoma accurately. These tools not only reduce diagnostic time but also improve treatment planning.[3]

Another significant advantage of AI is in the quantification of biomarkers through immunohistochemistry (IHC). Algorithms can objectively assess the expression levels of markers such as HER2, ER, and Ki-67 in breast cancer, eliminating observer bias and standardizing results across laboratories. Moreover, AI can assist in detecting lymph node metastases in sentinel lymph node biopsies with greater sensitivity than traditional methods. [4].

Integrating AI into pathology workflows also facilitates predictive modeling. AI models trained on histological features can predict patient outcomes and responses to therapy. This is particularly useful in personalized medicine, where treatment decisions are guided by predicted tumor behavior. Despite the promising applications, there are challenges to AI adoption in pathology. These include the need for large, annotated datasets, regulatory approvals, and integration with existing laboratory information systems. Additionally, pathologists must be trained to understand and validate AI outputs, fostering a collaborative model of AI-assisted diagnostics [5].

Conclusion

Revolutionizing anatomical pathology by enhancing the accuracy and efficiency of cancer detection. As the technology matures, it is expected to become an indispensable tool in routine pathological practice, contributing to improved patient care.

References

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