A Automated Histology Analysis: Advancements, Challenges, and Future Directions

Abstract

Automated histology analysis has emerged as a transformative field integrating machine learning, artificial intelligence, and digital pathology to enhance diagnostic accuracy and efficiency. Recent advancements have enabled deep learning–based tissue classification, cellular morphology assessment, and feature quantification with high precision. However, challenges such as data standardization, limited annotated datasets, variability in staining techniques, and lack of interpretability in AI models hinder widespread clinical adoption. This review summarizes international and national research trends, technological advancements, existing limitations, and future possibilities in automated histology analysis. The future of this field lies in multimodal imaging, explainable AI, and robust clinical validation, which collectively hold potential to revolutionize diagnostic pathology and biomedical research.

Keywords: Automated histology, digital pathology, deep learning, segmentation, whole slide imaging, explainable AI.