AI in Histopathology: Automated Cancer Diagnosis to Detect Cancerous Cells and Assess Tumor Grade

Muhammad Fahad; Muhammad Umer Qayyum; Nasrullah Abbasi

Muhammad Fahad
Muhammad Umer Qayyum
Nasrullah Abbasi

Keywords: Artificial Intelligence (AI), Histopathology, Machine Learning (ML), Deep Learning (DL), Cancer Diagnosis, Tumor Grading, AI in Healthcare

Abstract

Artificial intelligence has revolutionized the field of histopathology by making automated cancer diagnosis possible with high degrees of accuracy, especially in detecting cancerous cells from non-cancerous cells and tumor grading. Traditionally, histopathology relies on microscopic observation of stained tissue samples to identify and classify various kinds of cancers; therefore, it plays a very important role in diagnosis. However, there is an inherent limitation to manual examination such as subjectivity, variability, and a time-consuming process. The integration of AI with Machine Learning (ML) and Deep Learning (DL) algorithms introduces improvement in diagnostic accuracy and efficiency by processing whole slide images (WSIs) within histopathological diagnosis. A number of machine learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been able to adopt automated identification of malignant features, hence improving on the grading, staging, and prognosis of cancer. Besides, the AI-based computational pathology models outperform the conventional diagnostic approach due to a minimal incidence of errors, leading to consistency in results. This review paper discusses, for a variety of cancers such as breast, lung, and prostate, the state of the art in AI applications for cancer diagnosis in histopathology. It also summarized the growing benefits of AI in enhancing workflow efficiency, reducing variability in diagnosis, and improving immunohistochemistry IHC biomarker assessments. As AI technology continues to evolve, it has the potential to alter the future of clinical decision-making and streamline cancer diagnostics.

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