Applications of Machine Learning in Histopathology: From Diagnostic Accuracy to Prognostic Insights

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DOĞANYİĞİT Z.

Global Clinical Engineering Journal, cilt.8, sa.2, ss.75-82, 2026 (Scopus)

Özet

Background: Histopathological examination remains the gold standard for evaluating morphological alterations in tissues and plays a pivotal role in both disease diagnosis and therapeutic decision-making. With the exponential growth of digital pathology, the integration of artificial intelligence (AI), particularly machine learning (ML) algorithms, has emerged as a transformative approach to optimize histopathological workflows. Objective: This review aims to systematically evaluate recent advances in ML applications within histopathology, focusing on their roles in enhancing diagnostic precision and enabling prognostic stratification across various malignancies. Methods: A comprehensive literature analysis was conducted, encompassing peer-reviewed studies that investigate the implementation of ML models in histopathological image interpretation, classification, segmentation, and outcome prediction. Emphasis was placed on convolutional neural networks, and ensemble learning techniques. Findings: Machine learning-based approaches demonstrate high sensitivity and specificity in the detection and classification of neoplastic lesions, particularly in breast, colorectal, thyroid, gastric, and head and neck cancers. These tools facilitate intraoperative consultation, mitotic figure quantification, and tumor grading, thereby improving diagnostic accuracy and reproducibility. Moreover, emerging prognostic models incorporating histopathological features show potential in predicting disease recurrence, overall survival, and treatment response, supporting the paradigm shift toward personalized medicine. Conclusions: The incorporation of ML into histopathological practice holds substantial potential to revolutionize diagnostic and prognostic processes. As algorithmic models continue to evolve and validate in clinical settings, their integration may redefine standard-of-care practices and bridge the gap between pathology and computational medicine.