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Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation

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arxiv 2304.01220 v1 pith:QHG343IC submitted 2023-04-01 eess.IV cs.CV

Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation

Hieu H. Pham , Ha Q. Nguyen , Hieu T. Nguyen , Linh T. Le , Khanh Lam This is my paper
classification eess.IV cs.CV
keywords agreementsystemlearningmachinechestexplainableimpactinterobserver
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We conducted a prospective study to measure the clinical impact of an explainable machine learning system on interobserver agreement in chest radiograph interpretation. The AI system, which we call as it VinDr-CXR when used as a diagnosis-supporting tool, significantly improved the agreement between six radiologists with an increase of 1.5% in mean Fleiss' Kappa. In addition, we also observed that, after the radiologists consulted AI's suggestions, the agreement between each radiologist and the system was remarkably increased by 3.3% in mean Cohen's Kappa. This work has been accepted for publication in IEEE Access and this paper is our short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, USA.

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