Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

Abstract

Purpose of reviewMachine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care. Recent findingsThe frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists.

Systemic lupus in the era of machine learning medicine

“…Thus far, considerable work in SLE has been focused on developing ML models to predict disease, diagnosis and prognosis. Other applications of AI in SLE including drug discovery, clinical trial design and interpretation, 171 diagnostic imaging analysis, personalised medicine and medical devices and technologies are just beginning. Increased availability and access to other types of data in the future will provide even more opportunities for SLE research.…”