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Development of Evaluation Criteria for the Radiologist’s Work Function in Interpreting of Magnetic Resonance Imaging Results

2026·0 Zitationen·Medical Radiology and radiation safety
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4

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2026

Jahr

Abstract

Purpose: To develop criteria for quantifying the work of radiologists when performing functional duties to describe the results of MRI examinations in order to increase the economic interest of staff in improving labor efficiency and in the high quality of its results. Material and methods: study was performed using a computational and analytical method with an analysis of the labor structure in the Department of magnetic resonance imaging (MRI) of the Burnazyan Radiology Center and the development of differentiated coefficients reflecting the complexity of the main labor processes in describing the results of MRI. Results: The time of performing MRI report by a radiologist was analyzed depending on the complexity of the MRI examination ‒ the anatomical scanning area, the combination of scanning areas, the detected pathology, and the MRI scan protocol. Based on the results of the analysis, coefficients have been developed that make it possible to differentiate the financial remuneration of a radiologist for the work performed, taking into account both the number and quality of MRI descriptions per shift. Conclusion: The developed criteria and algorithm for evaluating the effectiveness of the work of radiologists in the department of magnetic resonance imaging make it possible to stimulate differentially the payment of their work to radiologists in terms of the volume of MRI studies performed. In order to take into account the quality of MRI descriptions, due to the complexity of grading in manual supervision, it is necessary to additionally involve an evaluating expert with compensation for his labor costs, or create an algorithm and software product using artificial intelligence to evaluate automatically the quality of MRI descriptions of patients based on the developed criteria.

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Radiology practices and educationRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education
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