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The Value of Structured Reporting for AI

  • Daniel Pinto dos Santos
Chapter

Abstract

Besides image data from which AI systems could potentially extract meaningful information, radiological departments possess vast amounts of clinically relevant information contained in the report texts associated with the respective imaging studies. However, the automated extraction of data contained in radiological reports is difficult due to the unstructured and heterogeneous nature of current day’s prose-like reports. Even though natural language processing has seen substantial improvements over the past years, it remains difficult to use radiological reports from clinical routine as valid annotations for the training of algorithms in computer vision.

Structured reporting is currently being discussed within the radiological communities and besides providing other benefits, for example, in the communication with referring physicians, would make radiological reports much more machine-readable. Also, through providing clearly defined structures, report templates would facilitate data from other systems to be integrated into the radiological report.

This chapter aims to provide an overview of the current state of structured reporting with a special focus on its potential implications for the development of and interaction with AI systems.

Keywords

Radiological reporting Structured reporting Natural language processing Data annotations Data integration 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daniel Pinto dos Santos
    • 1
  1. 1.University Hospital of CologneCologneGermany

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