AI in interstitial lung disease: from visual HRCT reads to quantitative decision support

AI can enhance HRCT interpretation in interstitial lung disease by detecting subtle abnormalities, classifying patterns, and generating quantitative biomarkers. Multimodal models may refine risk stratification. Prospective validation is needed for clinical translation.

• AI-driven imaging analysis can identify subtle interstitial lung abnormalities earlier than conventional visual interpretation.
• Quantitative biomarkers from HRCT correlate with disease severity and progression, enabling objective monitoring.
• Multimodal models combining imaging, clinical, and functional data show promise for individualized risk assessment.

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