Digital Pathology and Artificial Intelligence in Cancer Diagnosis

Author(s):

Enaam M. Junainah*

This study analyzes the integration of Digital Pathology (DP) and Artificial Intelligence (AI) in cancer diagnosis, focusing on their technological advancements, clinical applications, challenges and future directions. A comprehensive review of recent advances in digital pathology and AI was conducted, analyzing peer-reviewed studies and clinical trials. The study emphasized the capabilities of AI-driven algorithms such as Convolutional Neural Networks (CNNs) in improving diagnostic workflows and tumor classification. AI-enhanced DP systems demonstrated higher accuracy and efficiency in cancer detection and grading, particularly for breast, prostate and lung cancers. AI tools, including deep learning algorithms, have been successful in segmenting tumors and predicting patient outcomes. However, challenges remain, including data standardization, regulatory approval and model interpretability. The integration of DP and AI is transforming cancer diagnosis by enhancing the speed, accuracy and reproducibility of diagnostic workflows. Overcoming current challenges will lead to more widespread adoption, with potential applications in personalized medicine and global health.