Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation

ConclusionThe AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low.Clinical relevance statementThe AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography.Key Points• A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions.• Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations.• Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist’s reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevan ce.Graphical Abstract
Source: European Radiology - Category: Radiology Source Type: research