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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.

Marko Topalovic1, Nilakash Das1, Pierre-Régis Burgel2

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Pulmonologists show significant variability in interpreting pulmonary function tests (PFTs), leading to diagnostic errors. Artificial intelligence (AI) software offers superior accuracy and consistency in PFT interpretation and diagnosis.

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Area of Science:

  • Pulmonary Medicine
  • Medical Diagnostics
  • Artificial Intelligence in Healthcare

Background:

  • Interpretation of pulmonary function tests (PFTs) for diagnosing respiratory diseases relies heavily on expert opinion and pattern recognition within clinical context.
  • Significant inter-observer variability and potential for errors exist in human interpretation of PFTs, impacting diagnostic accuracy.

Purpose of the Study:

  • To compare the accuracy and inter-rater variability of pulmonologists in interpreting PFTs against a validated artificial intelligence (AI)-based software.
  • To evaluate the diagnostic performance of AI software in PFT interpretation using established clinical guidelines and gold standards.

Main Methods:

  • 120 pulmonologists (senior 73%, junior 27%) from 16 European hospitals independently interpreted 50 PFT cases with clinical data, generating 6000 interpretations.
  • An AI-based software analyzed the same PFT and clinical data.
  • American Thoracic Society/European Respiratory Society guidelines served as the gold standard for PFT pattern interpretation; clinical history, PFTs, and additional tests formed the diagnostic gold standard.

Main Results:

  • Pulmonologists matched PFT patterns to guidelines in 74.4±5.9% of cases, with moderate inter-rater agreement (κ=0.67).
  • Pulmonologists achieved correct diagnoses in only 44.6±8.7% of cases, exhibiting substantial inter-rater variability (κ=0.35).
  • The AI software achieved 100% accuracy in PFT pattern interpretation and an 82% correct diagnosis rate, significantly outperforming pulmonologists (p<0.0001).

Conclusions:

  • Human interpretation of PFTs by pulmonologists is associated with considerable variability and diagnostic inaccuracies.
  • AI-based software demonstrates superior accuracy in PFT pattern recognition and diagnosis, highlighting its potential as a valuable decision support tool.
  • Implementing AI software in clinical practice could significantly enhance the reliability and accuracy of respiratory disease diagnosis through PFT interpretation.