Radiology-Pathology Correlation to Facilitate Peer Learning: An Overview Including Recent Artificial Intelligence Methods.
J Am Coll Radiol. 2019 Sep;16(9 Pt B):1279-1285. doi: 10.1016/j.jacr.2019.05.010.
Correlation of pathology reports with radiology examinations has long been of interest to radiologists and helps to facilitate peer learning. Such correlation also helps meet regulatory requirements, ensures quality, and supports multidisciplinary conferences and patient care. Additional offshoots of such correlation include evaluating for and ensuring concordance of pathology results with radiology interpretation and procedures as well as ensuring specimen adequacy after biopsy. For much of the history of radiology, this correlation has been done manually, which is time consuming and cumbersome and provides coverage of only a fraction of radiology examinations performed. Electronic storage and indexing of radiology and pathology information laid the foundation for easier access and for the development of automated artificial intelligence methods to match pathology information with radiology reports. More recent techniques have resulted in near comprehensive coverage of radiology examinations with methods to present results and solicit feedback from end users. Newer deep learning language modeling techniques will advance these methods by providing more robust automated and comprehensive radiology-pathology correlation with the ability to rapidly, flexibly, and iteratively tune models to site and user preference.
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