AI-Assisted Interpretation of Pulmonary Function Tests: Enhancing Diagnostic Precision in Respiratory Medicine.

Background: Interpreting pulmonary function tests (PFTs) accurately is essential for diagnosing and monitoring respiratory disorders. However, manual interpretation is subject to interobserver variability. Artificial intelligence (AI) offers a promising tool to enhance diagnostic consistency and accuracy. Objectives: To evaluate the diagnostic accuracy of an AI-based interpretation system for PFTs in comparison with expert pulmonologist consensus and to assess its applicability in clinical settings. Methods: A cross-sectional diagnostic accuracy study was conducted among 200 adult patients undergoing spirometry and full PFT. AI software analyzed raw PFT data to classify patterns as normal, obstructive, restrictive, or mixed. Three pulmonologists independently reviewed the same data, and their consensus was considered the reference standard. Diagnostic performance metrics, including sensitivity, specificity, accuracy, and Cohen's kappa, were calculated. Results: The AI system correctly identified 38/40 normal, 76/80 obstructive, 44/50 restrictive, and 27/30 mixed patterns, with an overall diagnostic accuracy of 92%. Sensitivity and specificity were 91.5% and 93.2%, respectively. Cohen's kappa was 0.86, indicating strong agreement with expert interpretations. Conclusion: AI-assisted interpretation of PFTs demonstrates high diagnostic concordance with pulmonologist consensus and holds potential for standardized, efficient respiratory diagnostics.