Using artificial intelligence and digital initiatives to amplify voices
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EULAR – The European Alliance of Associations for Rheumatology – works with the network of national organisations of People with Arthritis/Rheumatism in Europe (PARE) to ensure the voices of people with RMD are heard and have influence – creating powerful alliances that make a real difference. Artificial intelligence (AI) is another potential ally in that fight. At the EULAR 2026 Congress in London, work was presented from three PARE initiatives that showcase how digital tools can be used in treatment education and decision-making, and potentially avoid the use of general web searches to answer important questions.

People with rheumatic and musculoskeletal diseases (RMD) often have questions about their condition, its treatment, and the long-term implications. Getting the right answers is important, especially since health literacy is a key determinant of favourable outcomes. But many resources are not user-friendly, and people are left to navigate lengthy documents. Those who resort to searching online are left to judge the quality of health information on their own. AI chatbots represent a promising and scalable approach to patient education, but evidence on their real-world use and patient experience is still scarce. In an oral abstract presentation on Thursday 4th June, Johannes Knitza presented work to develop ten disease-specific chatbots in rheumatology – grounded in the respective German clinical guidelines. The chatbots were promoted via patient organisations and rheumatologists, and test users could ask disease-related questions and provide immediate feedback on the AI’s responses. In the first 4 months, 5,131 chatbot interactions were recorded across 1,312 individual sessions. Direct feedback was provided for 2,165 answers, of which 92.9% received a “like” and 7.1% a “dislike”. An evaluation questionnaire was completed by 520 users, 94.0% of whom reported a diagnosed RMD, most commonly rheumatoid arthritis, axial spondyloarthritis, or systemic lupus erythematosus. Prior use of AI-based tools for health-related questions was reported by 41% of participants, and 86% strongly agreed that the chatbot was easy to use and the answers were easy to understand, with most considering it a useful addition to existing patient education materials. Overall, these disease-specific, guideline-based chatbots were well received and showed high levels of usability, perceived usefulness, and trust – with 58% preferring the chatbot to general internet searches.

Building digital tools that patients prefer to general interest searches is a positive step, since the quality of web content is highly variable. Another study presented at the Congress looked at the ability of large language models to answer real patient questions, comparing three general-purpose models to Google Search for the 20 most frequently asked questions from patients with systemic lupus erythematosus, idiopathic inflammatory myopathy, Sjögren’s disease, or systemic sclerosis. Across all diseases, both patients and rheumatologists rated the model responses favourably across empathy, trustworthiness, and comprehensibility. Importantly, physicians rated medical correctness as consistently accurate.

Presenting the work, Phillip Kremer said “While Google-based information was largely medically correct, large-language models offered added value in terms of clarity and empathy. When implemented with appropriate safeguards and physician oversight, these tools could complement established patient education strategies in rheumatology.”

These tools can also be used to address very specific needs within the RMD community. It is known that long-term use of steroids leads to multiple side effects, but there has been a gap in patient education about how to mitigate these adverse events. Steroids and Me (Sam) is a novel approach to patient empowerment in glucocorticoid therapy – a web-based platform with a journey tracker for patients to capture steroid side effects in real time, and share results with their doctors at follow up visits. The content, delivered in plain language, includes common and little-known steroid side effects, tips on prevention and management, and videos from physician experts. A poster from Martha Stone and colleagues presented the development, validation, and outcomes from the first 24 months of Sam. The tool has been implemented through Patient Advocacy Group collaborations for a number of conditions – not just in rheumatology. To date there are over 25,000 users, spending an average of 5.4 minutes learning on SAM – 10-times that recorded for global health websites, indicating deep engagement with the content that addresses an unmet need. The platform transforms patients from isolated and confused individuals into informed partners in their own care. Future directions include pairing with clinical outcome assessments of steroid toxicity in clinical trials to deliver the patients lived experience for a full picture of steroid burden, insights to support steroid stewardship across the medical landscape, and expanded disease community partnerships.

Source

Wilhelmi T, et al. Turning Guidelines to Answers: Patient Evaluation of AI-Based Guideline Chatbots in Rheumatology. Presented at EULAR 2026; OP0256-PARE. Ann Rheum Dis 2026; DOI: 10.1136/annrheumdis-2026-eular.D.57.

Kremer P, et al. Beyond “Dr Google”: Performance of Large Language Models in Patient Counselling for Connective Tissue Diseases. Presented at EULAR 2026; OP013-PARE. Ann Rheum Dis 2026; DOI: 10.1136/annrheumdis-2026-eular.D.132.

Stone M, et al. Steroids and Me (Sam): Development and Validation of a Patient-Centered Digital Platform for Glucocorticoid Education and Shared Decision-Making. Presented at EULAR 2026; POS1388-PARE. Ann Rheum Dis 2026; DOI: 10.1136/annrheumdis-2026-eular.D.42.