The Advancements and Challenges in Digital Pathology: Transforming the Future of Diagnostic Practice

Abstract

Background: Digital pathology, which involves the use of digital imaging and computational analysis for the examination of tissue samples, has become an essential tool in modern pathology. This article discusses the technological advancements, challenges, and future directions of digital pathology in diagnostic practice.
Methods: This review examines recent literature from 2015 to 2024 on the applications of digital pathology in clinical practice, including image acquisition, artificial intelligence (AI) in image analysis, and the integration of digital pathology into laboratory workflows.
Results: Digital pathology has enhanced the accuracy, speed, and efficiency of diagnostic processes, particularly in the areas of cancer diagnosis, dermatopathology, and neuropathology. The use of AI and machine learning algorithms has improved image analysis, enabling more precise detection of abnormalities in tissue samples. However, challenges remain in terms of standardization, regulatory approval, and training.
Conclusion: Digital pathology is reshaping the future of diagnostic practice, offering opportunities for improved accuracy, faster results, and greater collaboration among pathologists. Continued advancements in AI, standardization, and training are needed to fully realize its potential.