Pharmacological personalization of JAK inhibitors in rheumatoid arthritis: A multi-omics and AI-based scoping review and evidence-gap map.

INTRODUCTION: Personalization of Janus kinase inhibitor (JAKi) therapy in rheumatoid arthritis (RA) remains an unresolved clinical task. Multi-omics/multimodal (MO/MM) data and artificial intelligence/machine learning (AI/ML) may support treatment-response prediction, but their JAKi-specific application has not been systematically mapped. METHODS: We conducted a JBI scoping review with PRISMA-ScR reporting; the protocol was registered on OSF. Major bibliographic databases and additional sources were searched through 7 July 2025 without language or publication-status restrictions. We included direct JAKi studies using AI/ML or integrated MO/MM data and contextual non-JAKi RA studies combining MO/MM with AI/ML. Risk of bias and reporting completeness were assessed using PROBAST+AI and TRIPOD+AI. Findings were synthesized narratively. RESULTS: Eighteen publications were included, four of which were conference abstracts. The corpus comprised three direct JAKi AI/ML studies, three JAKi-associated MO/MM studies without AI/ML, eleven contextual non-JAKi AI/ML studies, and one mixed JAKi/TNFi study. AI/ML was applied in 15/18 publications. None of the direct JAKi AI/ML studies performed independent external validation. Reported AUROCs ranged from 0.656 to 1.00, with the highest estimates arising under internal or incompletely reported validation. Calibration and explainability were infrequently reported. CONCLUSION: Direct evidence for AI/ML-guided JAKi personalization remains sparse and heterogeneous and does not support selection of a specific JAKi or routine individualized treatment decisions. Progress requires adequately powered JAKi-specific cohorts, class-separated analyses, harmonized measurements, transparent preprocessing, and independent external validation. The "data × methods × JAKi" map identifies both emerging signals and the links that must be strengthened before MO/MM and AI/ML can support clinical decision-making in RA. TAKE-HOME MESSAGE: Current multi-omics and AI/ML evidence is insufficient to guide routine selection of a specific JAK inhibitor in rheumatoid arthritis. Independent external validation, calibration, and prospective assessment of clinical utility are required before these approaches can support treatment decisions.