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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic necessitates rapid and accurate disease identification.
  • Chest X-ray imaging is a crucial tool for assessing COVID-19.
  • Artificial intelligence (AI) is increasingly applied for automated COVID-19 classification from X-rays.

Purpose of the Study:

  • To critically analyze the methodologies used in AI-based COVID-19 classification from chest X-rays.
  • To identify common errors in the evaluation protocols of existing scientific literature.
  • To highlight issues leading to overestimated performance in AI models for COVID-19 detection.

Main Methods:

  • Systematic review of scientific literature on AI for COVID-19 chest X-ray classification.
  • Analysis of evaluation strategies and performance metrics reported in reviewed studies.
  • Identification of methodological flaws in study designs and result reporting.

Main Results:

  • Many studies employ incorrect evaluation protocols, inflating reported accuracy.
  • Overestimation of AI model performance is prevalent in the current literature.
  • Lack of standardized and rigorous evaluation hinders clinical adoption of AI tools.

Conclusions:

  • Current AI applications for COVID-19 detection via chest X-rays require more robust validation.
  • Standardized and accurate evaluation methodologies are essential for reliable clinical tools.
  • Addressing methodological flaws is critical for trustworthy AI in pandemic response.