Resumen

LOPEZ-CABRERA, José Daniel  y  PEREZ-DIAZ, Marlen. Explainable artificial intelligence, a perspective to the automatic classification of COVID-19 through chest X-rays problem. Medisur [online]. 2022, vol.20, n.2, pp. 341-351.  Epub 30-Abr-2022. ISSN 1727-897X.

This research aims to elucidate, from the analysis of explainable artificial intelligence techniques, the robustness and level of generalization of the proposed computer vision methods to identify COVID-19 using chest X-ray images. Also, alert researchers and reviewers about the problem of learning by shortcuts. In this study, recommendations are followed to identify if the automatic classification models of COVID-19 are affected by shortcut learning. To do this, articles that use explainable artificial intelligence methods were reviewed. It was shown that when using the full chest X-ray image or the bounding box of the lungs, the regions of the image that contribute the most to the classification appear outside the lung region, something that does not make sense. The results indicate that, so far, the existing models present the problem of learning by shortcuts, which makes them inappropriate to be used in clinical settings.

Palabras clave : artificial intelligence; classification; radiography, thoracic; COVID-19.

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