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Machine learning (ML) is a subfield of Artificial intelligence (AI), and itsapplications in radiology are growing at an ever-accelerating rate. The moststudied ML application is the automated interpretation of images. However,natural language processing (NLP), which can be combined with ML for textinterpretation tasks, also has many potential applications in radiology. Onesuch application is automation of radiology protocolling, which involvesinterpreting a clinical radiology referral and selecting the appropriateimaging technique. It is an essential task which ensures that the correctimaging is performed. However, the time that a radiologist must dedicate toprotocolling could otherwise be spent reporting, communicating with referrers,or teaching. To date, there have been few publications in which ML models weredeveloped that use clinical text to automate protocol selection. This articlereviews the existing literature in this field. A systematic assessment of thepublished models is performed with reference to best practices suggested bymachine learning convention. Progress towards implementing automatedprotocolling in a clinical setting is discussed.

A Review of Published Machine Learning Natural Language Processing Applications for Protocolling Radiology Imaging