Artificial Intelligence in Idiopathic Pulmonary Fibrosis: Advances, Challenges, and Future Directions

Idiopathic Pulmonary Fibrosis (IPF) is a progressive disease of unknown etiology, characterized 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 network, 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 optimize 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, multicenter studies. Future research should prioritize the development of standardized models of AI in large cohorts of IPF patients, combining clinical, imaging, morphologic, multiomics and other data, and enhance model transparency to strengthen clinical confidence. With continued advancements, AI holds potential to improve IPF management, enabling early diagnosis, individualized 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.