ARTIFICIAL INTELLIGENCE: NATURAL LANGUAGE PROCESSING FOR PEER-REVIEW IN RADIOLOGY

Abstract

Objective. To assess the importance of natural language processing (NLP) system for quality assurance of the radiological reports. Material and methods. Multilateral analysis of chest low-dose computed tomography (LDCT) reports based on a commercially available cognitive NLP system was performed. The applicability of artificial intelligence for discrepancy identification in the report body and conclusion (quantitative analysis) and radiologist adherence to the Lung-RADS guidelines (qualitative analysis) was evaluated. Results. Quantitative analysis: in the 8.3% of cases LDCT reports contained discrepancies between text body and conclusion, i.e., lung nodule described only in body or conclusion. It carries potential risks and should be taken into account when performing a radiological study audit. Qualitative analysis: for the Lung-RADS 3 nodules, the recommended principles of patient management were used in 46%, for Lung-RADS 4A – in 42%, and for Lung-RADS 4B – in 49% of cases. Conclusion. The consistency of NLP system within the framework of radiological study audit was 95–96%. The system is applicable for the radiological study audit, i.e. large-scale automated analysis of radiological reports and other medical documents.

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