DIGITAL PATHOLOGY AND ARTIFICIAL INTELLIGENCE APPLICATIONS IN MODERN DIAGNOSTIC PRACTICE

Abstract

Digital pathology has emerged as a transformative paradigm in diagnostic medicine, enabling the integration of artificial intelligence to address challenges related to diagnostic variability, workload burden, and interpretative complexity. This study presents a comprehensive experimental framework for the application of artificial intelligence in modern digital pathology, combining quantitative deep learning–based image analysis with qualitative expert-driven validation. Whole-slide histopathological images were processed through a structured pipeline involving preprocessing, convolutional neural network–based feature extraction, and slide-level diagnostic aggregation. Model performance was systematically evaluated using standard classification metrics, demonstrating strong predictive accuracy, balanced sensitivity and specificity, and stable generalization across independent test data. To enhance clinical interpretability, explainable AI techniques were employed, revealing diagnostically meaningful regions that showed high concordance with expert pathologist assessments. The integration of expert feedback confirmed that AI-generated predictions and visual explanations aligned with established histopathological criteria, reinforcing the clinical relevance of the proposed approach. Furthermore, a scalable system architecture was developed to illustrate the practical deployment of AI within digital pathology environments, supporting interactive decision support rather than autonomous diagnosis. The results indicate that AI-assisted digital pathology can substantially improve diagnostic efficiency and consistency while preserving transparency and human oversight. This study underscores the potential of explainable, human-centered AI to advance diagnostic accuracy and workflow optimization in contemporary pathology practice.