Radiology-Pathology Correlation to Facilitate Peer Learning: An Overview Including Recent Artificial Intelligence...

Area of Science:

• Radiology and Pathology Informatics
• Medical Artificial Intelligence
• Health Informatics

Background:

• Radiology-pathology correlation is crucial for peer learning, quality assurance, and patient care.
• Manual correlation methods are time-consuming, cumbersome, and provide limited coverage.
• Electronic data storage has enabled the development of automated correlation techniques.

Purpose of the Study:

• To review the evolution and advancements in automated radiology-pathology correlation.
• To highlight the benefits of artificial intelligence (AI) in improving correlation processes.
• To discuss the potential of deep learning language models in future applications.

Main Methods:

• Review of historical and current methods for radiology-pathology correlation.
• Discussion of AI-driven approaches for matching pathology information with radiology reports.
• Exploration of deep learning language modeling techniques for enhanced correlation.

Main Results:

• Automated methods significantly increase the coverage and efficiency of radiology-pathology correlation compared to manual approaches.
• Recent techniques provide near-comprehensive coverage and incorporate user feedback mechanisms.
• Deep learning models promise more robust, flexible, and customizable correlation solutions.

Conclusions:

• Automated radiology-pathology correlation, particularly with AI and deep learning, enhances quality, efficiency, and patient care.
• Advanced language models offer the potential for rapid, flexible, and user-preferred correlation tuning.
• Continued development in AI is key to achieving comprehensive and optimized radiology-pathology concordance.