Abstract Background/Aims Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease affecting approximately 1% of the global population, with significant morbidity and socioeconomic impact. Despite therapeutic advances, delays in diagnosis and suboptimal monitoring persist, contributing to irreversible joint damage and impaired quality of life. Digital health innovations—including artificial intelligence (AI), remote monitoring, and eHealth platforms—offer new opportunities to optimise disease management and personalise care. This study aimed to synthesise current evidence on the application of digital health, AI, and eHealth tools in RA, with a focus on their role in early diagnosis, disease monitoring, patient engagement, and healthcare system integration. Methods A structured literature review was conducted across PubMed, Scopus, and Web of Science (2015-2025) using predefined keywords (“Rheumatoid Arthritis,” “Digital Health,” “Artificial Intelligence,” “eHealth,” “Remote Monitoring”). Studies were included if they reported on clinical applications, technological innovations, or implementation outcomes. Data were extracted, appraised for quality, and narratively synthesised with emphasis on patient-centred outcomes and scalability. Results From 1,243 records screened, 87 studies met inclusion criteria. Three thematic domains emerged: 1. Early Detection and Diagnosis: AI-driven imaging algorithms achieved diagnostic accuracies exceeding 90% in differentiating RA from other inflammatory arthritides using ultrasound and MRI datasets. Machine learning models integrating serological markers and clinical data predicted RA onset in at-risk cohorts with sensitivities of 78-85%. 2. Remote Monitoring and Self-Management: eHealth platforms incorporating smartphone applications and wearable biosensors enabled continuous capture of patient-reported outcomes and activity data. These systems improved adherence to treat-to-target protocols, with reported 30-40% reductions in outpatient visits without compromising disease control. 3. Healthcare System Integration: Digital dashboards facilitating real-time communication between patients and rheumatology teams reduced mean time-to-treatment adjustment by 2.1 months. However, disparities in digital literacy and access were evident, particularly among older adults and socioeconomically disadvantaged groups. Conclusion Digital health, AI, and eHealth interventions hold substantial promise in transforming RA care by enabling earlier detection, optimised disease monitoring, and enhanced patient-clinician collaboration. While clinical outcomes appear favourable, challenges remain in equitable access, regulatory frameworks, and long-term data integration. Importantly, patients described digital tools as fostering a sense of empowerment and shared decision-making, underscoring the human dimension of technological innovation. Future research should prioritise multicentre trials, co-design with patients, and evaluation of cost-effectiveness to ensure sustainable and inclusive adoption. Disclosure S. Roy: None.
Subham Roy (Wed,) studied this question.