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Cited by 12Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsArtificial Intelligence in Healthcare

Artificial intelligence in rheumatology: status quo and quo vadis—results of a national survey among German rheumatologists

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Holzer, Marie-Therese, Meinecke, Anna, Müller, Felix, Haase, Isabell, Morf, Harriet, Witte, Thorben, Labinsky, Hannah, Klemm, Philipp +2 more·2024·Therapeutic Advances in Musculoskeletal Disease
DOI 10.1177/1759720x241275818PMC PMC11565687
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Abstract

Background: The development and potential of artificial intelligence (AI) is remarkable. Its application in all medical disciplines, including rheumatology, is attracting attention. To what extent AI is already used in clinical routine in rheumatology is unknown. In addition, the perceived barriers, potentials, and expectations regarding AI by rheumatologists have not yet been studied. Objectives: To examine the current usage and perceived barriers and facilitators of AI, including large language models (LLMs), among rheumatologists. Design: National, observational, non-interventional, and cross-sectional web-based study. Methods: A web-based survey was developed by the Working Group Young Rheumatology (AGJR) of the German Society for Rheumatology. The survey was distributed at the Congress of the German Society for Rheumatology and via social media, QR code, and email from August 30 until November 4, 2023. Results: Responses from 172 rheumatologists (55% female; mean age 43 years) were analyzed. The majority stated that they did not previously use AI (73%) in their daily practice. Eighty-eight percent of rheumatologists rated their AI knowledge as low to intermediate and 84% would welcome dedicated training on LLMs. The majority of rheumatologists anticipated AI implementation to improve patient care (60%) and reduce daily workload (62%). Especially for diagnosis (73%), writing medical reports (70%), and data analysis (70%), rheumatologists reported a potential positive benefit of AI. Main AI concerns addressed the responsibility for medical decisions (64%) and data security (58%). Conclusion: Overall, the results indicate that rheumatologists currently have little AI knowledge and make very little use of AI in clinical routine. However, the majority of rheumatologists anticipate positive AI effects and would welcome increased AI implementation and dedicated training programs.

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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 [6]

    The Results (AI usage; page not provided) show that diagnosis was rheumatologists’ most frequently selected future application for AI (72.7%). But this survey did not include patients, and its LLM-specific results placed diagnosis behind scientific writing and patient correspondence. It therefore does not establish that both groups ranked diagnostic decision support as the most relevant use for LLMs. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 27

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Authors
Holzer, M.-T., Meinecke, A., Müller, F., Haase, I., Morf, H., Witte, T., Labinsky, H., Klemm, P., Knitza, J., & Krusche, M.
Affiliations
  • University Medical Center Hamburg-Eppendorf (DE)
  • Medizinische Hochschule Hannover (DE)
  • University Hospital Augsburg (DE)
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
  • Helios Klinikum Emil von Behring (DE)
  • Universitätsklinikum Würzburg (DE)
  • Justus-Liebig-Universität Gießen (DE)
  • Philipps University of Marburg (DE)
Corresponding author
Holzer, Marie-Therese
ORCID
Venue
Therapeutic Advances in Musculoskeletal Disease
Publisher
SAGE Publications
Published
2024-01
Volume
16
Pages
1759720X241275818-1759720X241275818
ISSN
  • 1759-720X (ISSN-L)
  • 1759-7218
DOI
10.1177/1759720x241275818
PMC
PMC11565687
OpenAlex
W4404389106
Cited by
12
References
3
Citations by year
2026: 7 · 2025: 4 · 2024: 1
FWCI
1.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Therapeutic Advances in Musculoskeletal Disease (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence, rheumatology, large language models, AI adoption barriers, AI training needs, clinical implementation of AI, data security, AI-assisted diagnosis

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