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This summary is machine-generated.

Suboptimal chest radiographs (CXRs) remain a challenge despite digital advancements. Artificial intelligence (AI) shows promise in improving CXR quality and interpretation, potentially reducing errors and variations.

Keywords:
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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Chest radiographs (CXRs) are frequently performed imaging tests but often suffer from suboptimal quality and high rejection rates.
  • Suboptimal CXRs can lead to diagnostic delays, interpretation errors, and inter-radiologist variability.
  • Despite digital radiography advancements, the issue of suboptimal image quality persists.

Purpose of the Study:

  • To review the existing literature on suboptimal chest radiograph quality.
  • To explore the potential applications of artificial intelligence (AI) in addressing suboptimal CXRs.

Main Methods:

  • Literature review of studies concerning suboptimal chest radiograph quality.
  • Examination of current and emerging AI technologies relevant to radiographic acquisition, triage, and interpretation.

Main Results:

  • Suboptimal CXRs contribute to significant challenges in patient care and diagnostic accuracy.
  • AI presents a potential solution for enhancing CXR quality and consistency.

Conclusions:

  • Artificial intelligence offers a promising avenue to mitigate the persistent problem of suboptimal chest radiograph quality.
  • Further research and implementation of AI in radiography could improve diagnostic workflows and patient outcomes.