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

arXiv 2304.01220 ↗First posted 1 Apr 2023eess.IV cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 1 Apr 2023.

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10 of 15 references.

References · 10

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  2. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  3. 2020VinDr-CXR: An open dataset of chest X-rays with radiologist's annotationscited by 236
  4. 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
  5. 2020Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labelscited by 56
  6. 2022Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysiscited by 13
  7. 2021Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networkscited by 7
  8. 2022An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiographcited by 7
  9. 2022Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamesecited by 5
  10. 2021A clinical validation of VinDr-CXR, an AI system for detecting abnormal chest radiographscited by 4

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  1. 2026Modeling Clinical Concern Trajectories in Language Model Agentscited by 0