Patient experiences, attitudes, and profiles regarding artificial intelligence in rheumatology: a German national cross-sectional survey study
Open accessAbstract
While artificial intelligence (AI) is gaining attention in rheumatology, little is known about patient perspectives. This study addresses this gap by examining patients’ experiences and attitudes toward AI. A nationwide, cross-sectional, web-based survey was conducted between March and May 2025 among adult patients with rheumatic diseases in Germany. Data were analyzed descriptively and with cluster analysis. A total of 778 patients completed the survey (70.4% female, mean age 51.3 years). The most common diagnosis was rheumatoid arthritis (31.7%). While 26.8% reported current AI use for health-related purposes, 57.8% expressed interest in using it. Patients were particularly interested in AI-based symptom checkers (64.3%), therapy recommendations (50.6%), and chatbots for medical inquiries (44.5%). 57.6% of patients indicated that they would welcome their rheumatologists using AI-based clinical suppport. The most frequently cited benefits of AI included improved information access (63.5%) and faster diagnosis (57.7%), while concerns centered on faulty AI (74.3%) and reduced human interaction (59.6%). Cluster analysis identified three distinct patient profiles: ‘AI-savvy’ (41.4%), ‘AI-pragmatic’ (44.8%), and ‘AI-skeptical’ (13.8%). Cluster membership was significantly associated with age and education, with younger patients more often belonging to the ‘AI-savvy’ group. Patients with rheumatic diseases showed substantial interest in AI-supported care, although actual use in medical contexts remained limited. Age and education differences highlight the need for tailored implementation strategies to ensure equitable and patient-centered adoption of AI in rheumatology.
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Rheumatic patients and rheumatologists both agree that diagnostic decision support is the most relevant field of implementation in rheumatology for LLMs.
“surveys demonstrated that rheumatic patients and rheumatologists both agree that diagnostic decision support is the most relevant field of implementation in rheumatology for LLMs [6, 12].”
From Diagnostic performance of Prof. Valmed, ChatGPT-5 Thinking, and OpenEvidence in rheumatology: A comparative evaluation (2026) · cites this paper as [12]
The Results (Current and Potential Use Cases of AI in Rheumatology; page unavailable) report that patients were most interested in AI-based symptom checkers, and the Conclusion says patients and rheumatologists share priorities around diagnostics and education. The paper does not establish that both groups ranked diagnostic decision support as the single most relevant application, or that their rankings concerned LLMs specifically rather than AI more broadly. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn a recent large national survey, the majority of patients stated that they would welcome their rheumatologists using AI for clinical decision support.
“Importantly, in a recent large national survey, the majority of patients stated that they would welcome their rheumatologists using AI for clinical decision support [12].”
From Diagnostic performance of Prof. Valmed, ChatGPT-5 Thinking, and OpenEvidence in rheumatology: A comparative evaluation (2026) · cites this paper as [12]
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- Authors
- Labinsky, H., Klemm, P., Graalmann, L., Thiele, T., Hornig, J., Fink, D., Morf, H., Mucke, J., Kiltz, U., Pecher, A.-C., Pfeil, A., Elling-Audersch, C., Bendzuck, G., Krusche, M., Hueber, A. J., & Knitza, J.
- Affiliations
- Universitätsklinikum Würzburg (DE)
- Justus-Liebig-Universität Gießen (DE)
- Medizinische Hochschule Hannover (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Rheumazentrum Ruhrgebiet (DE)
- Universitätsklinikum Tübingen (DE)
- Jena University Hospital (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- Philipps University of Marburg (DE)
- Corresponding author
- Knitza, Johannes
- ORCID
- Venue
- Rheumatology International
- Published
- 2025-11-10
- Volume
- 45
- Issue
- 12
- Pages
- 269-269
- ISSN
- 0172-8172 (ISSN-L)
- 1437-160X
- PMC
- PMC12602644
- OpenAlex
- W4416094230
- Cited by
- 20
- References
- 24
- Citations by year
- 2026: 20
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Rheumatology International (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OPUS FAU (Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV), on behalf of the Universitätsbibliothek Erlangen-Nürnberg) (submittedVersion, cc-by)
- Common Library Network (Der Gemeinsame Bibliotheksverbund) (submittedVersion, cc-by)
- Funders
- Philipps-Universität Marburg
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- artificial intelligence, rheumatic diseases, patient attitudes, patient experience, rheumatoid arthritis, chatbots, clinical decision support, patient characteristics, age and education differences, artificial intelligence adoption, trust in AI
- MeSH terms
- Adult, Aged, Artificial Intelligence, Cross-Sectional Studies, Female, Germany, Humans, Health Knowledge, Attitudes, Practice, Male, Middle Aged, Surveys and Questionnaires, Rheumatic Diseases, Rheumatology
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