SYSTEMIC LUPUS ERYTHEMATOSUS AND SJOGREN SYNDROME: Edited by Roberto CaricchioRecent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupusKingsmore, Kathryn M.; Lipsky, Peter E. Author Information AMPEL BioSolutions, LLC and The RILITE Research Institute, Charlottesville, Virginia, USA Correspondence to Peter E. Lipsky, MD, AMPEL BioSolutions, LLC and The RILITE Research Institute, Charlottesville, VA 22902, USA. E-mail: [email protected] Current Opinion in Rheumatology 34(6):p 374-381, November 2022. | DOI: 10.1097/BOR.0000000000000902 Buy Metrics Abstract Purpose of review Machine 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 findings The 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. Summary Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.