Machine learning for predicting treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in rheumatoid arthritis: a scoping review.

ABSTRACT:

Background

Biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) have improved outcomes in rheumatoid arthritis (RA). However, heterogeneity in treatment response remains a significant challenge. Machine learning (ML) may enable improved prediction, but the comprehensive review of ML applications in RA is fragmented and limited. This scoping review synthesizes the literature on ML methods for predicting treatment response to b/tsDMARDs in RA.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines, we systematically searched PubMed, MEDLINE, and Embase (from databases' inception through March 2024). Using the Covidence online platform, two reviewers independent

SUBMITTER: Eriakha EB 

PROVIDER: S-EPMC12607112 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature