Harnessing artificial intelligence to advance insights in systemic sclerosis skin and lung disease

The purpose of this review is to summarize the uses of artificial intelligence for advancing systemic sclerosis (SSc) skin and lung disease research through 2024.

Applications of AI in SSc research have expanded markedly in recent years. The most common artificial intelligence method identified was supervised machine learning for predictive modeling. Supervised machine learning uses input data labeled with a known outcome to train a model to predict outcomes when encountering new data. Using machine learningassisted feature selection and posttraining feature importance techniques also highlighted key predictors within complex datasets, informing possible mechanisms underlying heterogeneous patient outcomes. Additionally, unsupervised machine learning approaches have been used to identify patient subsets with distinct clinical trajectories. Unsupervised machine learning identifies groups with similar characteristics within a dataset, without considering a specific outcome. Digital image analysis using deep learning has also been undertaken in lung imaging studies to quantify interstitial lung disease (ILD) extent and automate ILD subtype classification, as well as skin biopsy analysis to quantify histologic changes. These scalable tools could efficiently automate prognostic assessments for use across centers of varying local expertise.

Artificial intelligence represents a tool for analyzing high-dimensional, complex datasets to derive robust results, even within relatively small SSc cohorts. To date, artificial intelligence driven insights to SSc skin and lung disease have focused on identifying patient subsets, quantifying disease severity, and building predictive models to inform personalized patient care.