In recent years, the field of pathological informatics has witnessed a surge in research focusing on the integration of artificial intelligence (AI) and machine learning (ML) algorithms to enhance diagnostic capabilities. One of the key areas of development is the application of deep learning, particularly convolutional neural networks (CNNs), in the analysis of digitized pathology images. CNNs have shown remarkable performance in tasks such as image segmentation, feature extraction, and pattern recognition, enabling pathologists to more accurately and efficiently diagnose various diseases, including cancers.

The four primary divisions of study of the core curriculum for pathology informatics

Moreover, the adoption of digital pathology workflows, which involve the digitization of entire pathology slides for viewing and analysis on digital platforms, has gained traction. Whole slide imaging (WSI) technologies allow pathologists to remotely access and analyze slides, facilitating collaboration and improving access to expert opinions, particularly in underserved areas. Additionally, the integration of pathology data into electronic health records (EHRs) is becoming increasingly important, as it enables seamless data sharing and interoperability with other healthcare systems, leading to more comprehensive patient care.

A digital pathology workflow always starts with the gross evaluation of the primary sample.

Standardization efforts focusing on data formats and terminologies are also advancing, aiming to establish common standards for data exchange and integration across different pathology systems. These efforts are crucial for ensuring the accuracy, reliability, and interoperability of digital pathology workflows, ultimately leading to improved patient outcomes. Overall, these advancements in pathological informatics are poised to revolutionize the field by enhancing diagnostic capabilities, improving workflow efficiency, and enabling more personalized approaches to patient care.