Abstract
The advent of QCT (quantitative computed tomography) and AI (artificial intelligence) using high-resolution computed tomography (HRCT) data has revolutionized the way interstitial diseases are studied. These quantitative methods provide more accurate and precise results compared to previous semi-quantitative methods, which were limited by human error such as interobserver disagreement or low reproducibility. The integration of QCT and AI and the development of digital biomarkers has facilitated not only diagnosis but also prognostication and prediction of disease behaviour not just in idiopathic pulmonary fibrosis (IPF) where they were initially studied but also in other fibrotic lung diseases. These tools provide reproducible, objective prognostic information which may facilitate clinical decision-making. However, despite the benefits of QCT and AI, there are still obstacles that need to be addressed. Important issues include optimal data management, data sharing and maintaining data privacy. In addition, the development of explainable AI will be essential to develop trust within the medical community and facilitate implementation in routine clinical practice.
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