Precision medicine with the aid of artificial intelligence (AI) is becoming more common each day.

Osteoscan, an artificial intelligence program, determines the radiographic stage of knee osteoarthritis with 95% accuracy, indicate findings from the EULAR 2020 E-Congress.

Integration of clinical and protein biomarker data using a machine-learning approach may help to distinguish patients with psoriatic arthritis from those with psoriasis, suggests research presented at the ACR Convergence 2020 virtual meeting.

Artificial intelligence allows analyses of huge amounts of data with minimal aggregation.

Antsa Rakotozafiarison and Laure Gossec explore how wearable activity trackers coupled with machine learning can be used to monitor flares in people with rheumatic diseases.

The use of artificial intelligence and machine learning may be a game changer in this context.

He also notes that the bioinformatics capacity for analyzing gene expression data “is probably a limiting factor,” but the increasing use of artificial intelligence should improve our ability to understand genetic data.

Martin Krusche and Gerd Burmester discuss the potential for machine learning in the treatment of rheumatic diseases, including identification of clinical subgroups and prediction of response to therapy.

Machine learning models have demonstrated potential for predicting radiographic progression in patients with axial spondyloarthritis.

The volume of data of all types has shown very strong growth during the past two decades, including in medicine, and many attempts have been made to use the learning resources represented by these data to train artificial intelligence (AI) to diagnose more or less complex pathologies through machine learning strategies .

Nathan Foulquier and Alain Saraux evaluate the use of machine learning for the classification and diagnosis of rheumatic diseases.

They add that “[d]eep learning, a subdiscipline of artificial intelligence, has [...] demonstrated multiple successes in clinical applications,” including diabetic retinopathy screening , classification of cardiovascular arrhythmias, and predicting mortality risk in hospitalized patients.