ARTIFICIAL INTELLIGENCE IN REMOTE MONITORING OF RHEUMATOID ARTHRITIS: DIGITAL BIOMARKERS, PATIENT ENGAGEMENT, AND BARRIERS TO IMPLEMENTATION

Martyna Szymczyk; Jagoda  Pałubska; Oliwer Muller; Anna Szot; Dominik Szydełko; Katarzyna Rosa; Agata Słoma; Daria Danielczyk; Wiktor Czyżewski; Natalia Malatyńska

doi:10.31435/ijitss.1(49).2026.5348

Authors

Martyna Szymczyk Jan Kochanowski University, Kielce, Świętokrzyskie, Poland https://orcid.org/0009-0005-0772-7119
Jagoda Pałubska Independent Public Health Care Institution of the Ministry of the Interior and Administration in Kielce named after St. John Paul II, Kielce, Poland https://orcid.org/0009-0000-3833-7977
Oliwer Muller Kielce Hospital of St. Aleksandra Sp. z o.o., Kielce, Poland https://orcid.org/0009-0005-6197-8461
Anna Szot Jan Kochanowski University, Kielce, Świętokrzyskie, Poland https://orcid.org/0009-0003-2613-1068
Dominik Szydełko Wojewódzki Szpital Zespolony, Kielce, Świętokrzyskie, Poland https://orcid.org/0009-0002-9907-858X
Katarzyna Rosa Uniwersyteckie Centrum Kliniczne, Gdańsk, Pomerania, Poland https://orcid.org/0009-0005-9307-4774
Agata Słoma Uniwersyteckie Centrum Kliniczne, Gdańsk, Pomerania, Poland https://orcid.org/0009-0004-6807-7706
Daria Danielczyk Wojewódzki Szpital Zespolony, Kielce, Świętokrzyskie, Poland https://orcid.org/0009-0002-4955-4883
Wiktor Czyżewski Independent Public Health Care Institution of the Ministry of the Interior and Administration in Kielce named after St. John Paul II, Kielce, Poland https://orcid.org/0009-0003-0277-8203
Natalia Malatyńska Jan Kochanowski University, Kielce, Świętokrzyskie, Poland https://orcid.org/0009-0005-3824-8225

DOI:

https://doi.org/10.31435/ijitss.1(49).2026.5348

Keywords:

Rheumatoid Arthritis, Artificial Intelligence, Remote Monitoring, Digital Biomarkers, Patient Engagement, Telemedicine

Abstract

The growing availability of patient-generated health data is reshaping how rheumatoid arthritis (RA) can be monitored beyond the clinic. This narrative review examines how artificial intelligence (AI) supports remote RA monitoring through digital biomarkers, patient-reported tools, and connected technologies, with particular attention to patient engagement and implementation barriers. A structured PubMed-based search identified literature on RA, AI, machine learning, remote monitoring, wearables, smartphone applications, telemedicine, and digital health. Across the reviewed studies, AI was used mainly to interpret longitudinal patient-reported outcomes, activity-tracker data, and daily smartphone symptom reports. Collectively, these approaches suggest that remote monitoring can enrich between-visit assessment, support flare detection, and provide a more continuous picture of disease experience than episodic clinic review alone. Yet the evidence base remains uneven, with many studies limited by small samples, short follow-up, exploratory modeling, and heterogeneous outcomes. The literature also makes clear that the practical success of remote monitoring depends on more than algorithmic performance; usability, adherence, digital literacy, trust, privacy, workflow integration, and equitable access all shape whether these tools can function in routine care. The most credible near-term model is therefore not fully automated monitoring, but hybrid, clinician-linked systems co-designed with users. AI-supported remote monitoring is a promising extension of RA care, but its long-term value will depend on whether it can be validated rigorously and implemented in ways that are patient-centered, clinically meaningful, and socially inclusive.

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