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NLP has a significant role in advancing healthcare and has been found to bekey in extracting structured information from radiology reports. Understandingrecent developments in NLP application to radiology is of significance butrecent reviews on this are limited. This study systematically assesses recentliterature in NLP applied to radiology reports. Our automated literature searchyields 4,799 results using automated filtering, metadata enriching steps andcitation search combined with manual review. Our analysis is based on 21variables including radiology characteristics, NLP methodology, performance,study, and clinical application characteristics. We present a comprehensiveanalysis of the 164 publications retrieved with each categorised into one of 6clinical application categories. Deep learning use increases but conventionalmachine learning approaches are still prevalent. Deep learning remainschallenged when data is scarce and there is little evidence of adoption intoclinical practice. Despite 17% of studies reporting greater than 0.85 F1scores, it is hard to comparatively evaluate these approaches given that mostof them use different datasets. Only 14 studies made their data and 15 theircode available with 10 externally validating results. Automated understandingof clinical narratives of the radiology reports has the potential to enhancethe healthcare process but reproducibility and explainability of models areimportant if the domain is to move applications into clinical use. More couldbe done to share code enabling validation of methods on different institutionaldata and to reduce heterogeneity in reporting of study properties allowinginter-study comparisons. Our results have significance for researchersproviding a systematic synthesis of existing work to build on, identify gaps,opportunities for collaboration and avoid duplication.

A Systematic Review of Natural Language Processing Applied to Radiology Reports