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

OBJECTIVES
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.


METHODS
Postero-anterior (PA) chest X-rays with confirmed diagnosis were evaluated by ten 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.


RESULTS
Residents' diagnostic performance for all radiological findings had a mean sensitivity of 37.9% (versus 39.8% with AI support), a mean specificity of 93.9% (versus 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.


CONCLUSION
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.


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