ArticleRheumatology international2025
Patient experiences, attitudes, and profiles regarding artificial intelligence in rheumatology: a German national cross-sectional survey study.
Article in Rheumatology international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Infrared thermography in the detection of arthritis: a systematic review of diagnostic performance compared with joint ultrasound.Rheumatology international · 2026Pooled it
- Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial.BMC medical education · 2026Trial
- Development and Nationwide Multicentre Evaluation of Guideline-Grounded Large Language Model Chatbots to Support Patient Self-Management and Education in Rheumatology.Journal of medical systems · 2026Article
- OsteoCHAT: real-world patient evaluation and benchmarking of a guideline-grounded osteoporosis chatbot.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Large language models show high clinical safety but differences in completeness for reproductive counselling in women with inflammatory rheumatic and musculoskeletal diseases: a comparative expert evaluation.Rheumatology international · 2026Article
- Moderate-to-substantial agreement of ChatGPT-5 for Kellgren-Lawrence grading on synthetic knee radiographs: a controlled cross-sectional observer agreement study.Rheumatology international · 2026Article
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- Toward causal artificial intelligence for biologic treatment response in rheumatoid arthritis: current evidence and future directions.Rheumatology international · 2026Review
- Artificial intelligence in rheumatology and paediatric rheumatology: insights from an international survey by EMEUNET.EULAR rheumatology open · 2026Article
- Artificial intelligence in healthcare faces regulatory challenges: balancing innovation with human oversight.EULAR rheumatology open · 2026Review
- Evaluating usability, adherence and clinical benefit of a new digital heath application in rheumatoid arthritis: a pilot feasibility study.Rheumatology international · 2026Article
- Diagnostic performance of Prof. Valmed, ChatGPT-5 Thinking, and OpenEvidence in rheumatology: A comparative evaluation.Rheumatology international · 2026Article
- Patients' perception towards large language models in otorhinolaryngology, head and neck surgery: a single-centre survey.Frontiers in digital health · 2026Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.