Reducing Errors Resulting From Commonly Missed Chest Radiography Findings

Warren B Gefter; Hiroto Hatabu

doi:10.1016/j.chest.2022.12.003

Reducing Errors Resulting From Commonly Missed Chest Radiography Findings

Chest. 2023 Mar;163(3):634-649. doi: 10.1016/j.chest.2022.12.003. Epub 2022 Dec 10.

Authors

Warren B Gefter¹, Hiroto Hatabu²

Affiliations

¹ Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, PA.
² Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. Electronic address: hhatabu@partners.org.

PMID: 36513187
DOI: 10.1016/j.chest.2022.12.003

Abstract

Chest radiography (CXR), the most frequently performed imaging examination, is vulnerable to interpretation errors resulting from commonly missed findings. Methods to reduce these errors are presented. A practical approach using a systematic and comprehensive visual search strategy is described. The use of a checklist for quality control in the interpretation of CXR images is proposed to avoid overlooking commonly missed findings of clinical importance. Artificial intelligence is among the emerging and promising methods to enhance detection of CXR abnormalities. Despite their potential adverse consequences, errors offer opportunities for continued education and quality improvements in patient care, if managed within a just, supportive culture.

Keywords: artificial intelligence; chest radiograph; education; errors; misses.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Humans
Radiography
Radiography, Thoracic* / methods