Evaluation of standardized DICOM labels assigned by a hybrid AI tool and its impact on radiologists' reading times.

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Abstract

Medical artificial intelligence (AI) tools are increasingly used to manage the growing imaging workload in radiology. Although highly relevant to daily clinical practice, AI-driven workflow tools remain underrepresented in research. Inconsistencies in DICOM metadata are a major obstacle to workflow optimization, AI integration, and inter-institutional data sharing. Automatic DICOM metadata standardization is an important step towards addressing these challenges. This study evaluates the labeling accuracy of a commercially available AI-aided hybrid software tool and its impact on radiologists' reading times. A retrospective cohort study assessed the labeling accuracy of the DICOM standardization tool. A retrospective before-and-after design evaluated its impact on reading times. The tool was applied by a radiology provider between 2022 and 2024. Standardized DICOM labels (modality, body part, laterality, plane, contrast protocol) across 422 CR images and 1503 CT series were manually reviewed (gold standard). In a separate analysis, reading times before (10,966 cases) and after (10,342 cases) DICOM standardization were compared. Labeling accuracy ranged from 83 to 100% for body part, 91 to 100% for plane, and 88-100% for protocol classification. Following implementation, average reading times significantly decreased for CT Abdomen (-2.9 min), Total Body (-2.2 min), Head (-0.73 min), and Temporal Bone (-2.5 min) (p ≤ 0.02), with relative efficiency gains of 8-22%. Extrapolated annually, this equals 270 h saved. No significant changes were observed for CT Chest and Sinus/Orbits. This study suggests that an AI-aided tool can adequately standardize DICOM labels and may be associated with statistically significant reductions in radiologists' reading times following implementation. Question Inconsistent DICOM metadata pose long-standing challenges in imaging data management, hindering processes such as workflow efficiency, AI integration, and inter-institutional image sharing. What is the role of AI solutions to address DICOM inconsistencies? Findings An AI-aided hybrid software tool adequately standardized DICOM labels and may be associated with statistically significant reductions in radiologists' reading times following implementation. Clinical relevance Standardizing DICOM metadata may help streamline radiology workflows and image accessibility, benefiting both radiologists and other healthcare professionals.

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