Artificial intelligence in idiopathic pulmonary fibrosis: advances, challenges and future directions

Idiopathic pulmonary fibrosis (IPF) is a progressive disease of unknown aetiology, characterised by a radiological and/or morphological pattern of usual interstitial pneumonia. Its diagnosis is challenging, and disease progression is often variable and unpredictable. In recent years the introduction of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, has shown the potential to improve the diagnosis, prognosis and therapeutic strategies for IPF. As part of DL, convolutional neural networks enhance the accuracy of high-resolution computed tomography analysis, facilitating early and precise diagnosis. Likewise, predictive ML and DL models are being developed using clinical, morphological, transcriptional and imaging data to assess disease progression and stratify patients by risk, thereby improving prognosis evaluation. Furthermore, AI-driven drug discovery may optimise treatment strategies by identifying novel therapeutic targets, as recently demonstrated with the discovery of an NCK-interacting kinase inhibitor with strong antifibrotic properties. However, several challenges hamper widespread clinical integration and real-life implementation, including data heterogeneity, model interpretability and the need for robust validation through large-scale, multicentre studies. Future research should prioritise the development of standardised models of AI in large cohorts of IPF patients, combining clinical, imaging, morphological, multi-omics and other data, and enhance model transparency to strengthen clinical confidence. With continued advancements, AI holds potential to improve IPF management, enabling early diagnosis, individualised prognosis and targeted therapy, all aimed at improving patient outcomes. In this review, we explore the evolving role of AI in IPF management, its potential to support clinical decisions and the challenges to its clinical integration.