ROLE OF ARTIFICIAL INTELLIGENCE IN DETECTION OF ABNORMALITIES IN HISTOPATHOLOGICAL IMAGES

Histopathological examination plays a vital role in the diagnosis of various diseases, including cancer, infections, and inflammatory disorders, by analyzing tissue samples under a microscope. Traditional manual evaluation of histopathological slides is time-consuming and relies heavily on the experience and expertise of pathologists, which may result in observer variability and diagnostic errors. In recent years, Artificial Intelligence (AI) has emerged as an effective supportive tool for the detection of abnormalities in histopathological images.
AI-based techniques, particularly machine learning and deep learning algorithms such as convolutional neural networks (CNNs), are capable of analyzing digital histopathological images and identifying abnormal cellular features like irregular nuclei, altered tissue architecture, and malignant patterns. These systems can rapidly process large volumes of images with high accuracy and consistency, thereby improving diagnostic efficiency and reducing workload.
The use of AI in histopathology assists in early disease detection, objective image analysis, and accurate classification of benign and malignant lesions. AI does not replace the role of pathologists but serves as a valuable decision-support system that enhances diagnostic confidence and workflow efficiency. This study highlights the importance and potential benefits of Artificial Intelligence in improving accuracy, reliability, and speed in the detection of abnormalities in histopathological images in modern diagnostic practice.