AI-Enhanced Approaches to Interstitial Lung Disease: A Review of Machine Learning Advances

The integration of AI into healthcare has marked a transformative era, with machine learning (ML) playing a pivotal role in reshaping the landscape of interstitial lung disease (ILD) management. ML models excel in analysing complex datasets, such as medical imaging and electronic health records, offering unprecedented advancements in the diagnosis, prognosis, and treatment of ILDs. These models have demonstrated superior accuracy compared to traditional methods, particularly in diagnosing idiopathic pulmonary fibrosis, where delays in diagnosis significantly impact patient outcomes. Early and precise diagnosis through ML-driven tools allows for the timely initiation of therapy, which is crucial for improving prognosis and extending patients’ quality of life. Despite the challenges in data quality and model interpretability, the future of ML in pulmonary healthcare is promising, with continued advancements poised to enhance patient management and outcomes. This article aims to examine the transformative potential of ML in the management of ILD.