Radiology report entry with automatic phrase completion driven by language modeling

John Eng, Jason M. Eisner

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Keyboard entry or correction of radiology reports by radiologists and transcriptionists remains necessary in many settings despite advances in computerized speech recognition. A report entry system that implements an automated phrase completion feature based on language modeling was developed and tested. The special text editor uses context to predict the full word or phrase being typed, updating the displayed prediction after each keystroke. At any point, pressing the tab key inserts the predicted phrase without having to type the remaining characters of the phrase. Successive words of the phrase are predicted by a trigram language model. Phrase lengths are chosen to minimize the expected number of keystrokes as predicted by the language model. Operation is highly and automatically customized to each user. The language model was trained on 36,843 general radiography reports, which were consecutively generated and contained 1.48 million words. Performance was tested on 200 randomly selected reports outside of the training set. The phrase completion technique reduced the average number of keystrokes per report from 194 to 58; the average reduction factor was 3.3 (geometric mean) (95% confidence interval, 3.2-3.5). The algorithm significantly reduced the number of keystrokes required to generate a radiography report (P < .00005).

Original languageEnglish (US)
Pages (from-to)1493-1501
Number of pages9
Issue number5
StatePublished - 2004


  • Computers
  • Information management
  • Information management, systems
  • Radiology reporting systems

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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