Artificial Intelligence Enabling Radiology Reporting

Bernardo C Bizzo; Renata R Almeida; Tarik K Alkasab

doi:10.1016/j.rcl.2021.07.004

Artificial Intelligence Enabling Radiology Reporting

Radiol Clin North Am. 2021 Nov;59(6):1045-1052. doi: 10.1016/j.rcl.2021.07.004.

Authors

Bernardo C Bizzo¹, Renata R Almeida², Tarik K Alkasab³

Affiliations

¹ Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Founders 210, Boston, MA 02114, USA.
² Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
³ Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Founders 210, Boston, MA 02114, USA. Electronic address: TALKASAB@mgh.harvard.edu.

PMID: 34689872
DOI: 10.1016/j.rcl.2021.07.004

Abstract

The radiology reporting process is beginning to incorporate structured, semantically labeled data. Tools based on artificial intelligence technologies using a structured reporting context can assist with internal report consistency and longitudinal tracking. To-do lists of relevant issues could be assembled by artificial intelligence tools, incorporating components of the patient's history. Radiologists will review and select artificial intelligence-generated and other data to be transmitted to the electronic health record and generate feedback for ongoing improvement of artificial intelligence tools. These technologies should make reports more valuable by making reports more accessible and better able to integrate into care pathways.

Keywords: Artificial intelligence; Deep learning; Integration; Machine learning; Radiology; Report; Structured.

Publication types

Review

MeSH terms

Artificial Intelligence*
Diagnostic Imaging / methods*
Humans
Image Interpretation, Computer-Assisted / methods*
Radiology / methods*