The effect of an artificial intelligence algorithm on chest X-ray interpretation of radiology residents

Chest X-rays are the most commonly performed diagnostic examinations. An artificial intelligence (AI) system that evaluates the images fast and accurately help reducing workflow and management of the patients. An automated assistant may reduce the time of interpretation in daily practice. We aim to investigate whether radiology residents consider the recommendations of an AI system for their final decisions, and to assess the diagnostic performances of the residents and the AI system.

Posteroanterior (PA) chest X-rays with confirmed diagnosis were evaluated by 10 radiology residents. After interpretation, the residents checked the evaluations of the AI Algorithm and made their final decisions. Diagnostic performances of the residents without AI and after checking the AI results were compared.

Residents' diagnostic performance for all radiological findings had a mean sensitivity of 37.9% (vs 39.8% with AI support), a mean specificity of 93.9% (vs 93.9% with AI support). The residents obtained a mean AUC of 0.660 vs 0.669 with AI support. The AI algorithm diagnostic accuracy, measured by the overall mean AUC, was 0.789. No significant difference was detected between decisions taken with and without the support of AI.

Although, the AI algorithm diagnostic accuracy were higher than the residents, the radiology residents did not change their final decisions after reviewing AI recommendations. In order to benefit from these tools, the recommendations of the AI system must be more precise to the user.

This research provides information about the willingness or resistance of radiologists to work with AI technologies via diagnostic performance tests. It also shows the diagnostic performance of an existing AI algorithm, determined by real-life data.