Artificial Intelligence in Histopathology for Accurate Disease Diagnosis

Authors

  • Muhammad Anas DePaul University, Chicago, IL, United States of America Author

DOI:

https://doi.org/10.70445/gtst.2.4.2026.86-106

Keywords:

Artificial Intelligence, Histopathology, Deep learning, Cancer diagnosis, Machine learning, diagnostic accuracy

Abstract

AI is revolutionizing histopathology through its ability to enhance diagnostic precision, efficiency, and uniformity in disease identification and classification. The use of machine learning and deep learning algorithms allows for automated analysis of digital tissue images, which is helpful for cancer detection, tumor grading, classification and prediction of prognosis. AI-driven image processing and feature extraction aid in identifying fine details of morphology and minimize diagnostic variation. But the limitations of data, algorithmic bias, interpretability, data privacy and clinical validation are all important hurdles. The combination of AI, digital pathology, and clinical experience could lead to a more seamless diagnostic process and personalized medicine, aiding in accurate disease diagnosis and positive patient results.

Downloads

Published

2026-10-11