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Large language models in rheumatology-centered research: A scoping review of model openness and reporting practices.

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Τίτλος: Large language models in rheumatology-centered research: A scoping review of model openness and reporting practices.
Συγγραφείς: Madrid-García A; Independent Researcher, Spain. Electronic address: fredymad@msn.com., Merino-Barbancho B; Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense, Madrid, Spain. Electronic address: beatriz.merino@upm.es., Freites-Núñez D; Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain., Benavent D; Rheumatology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
Πηγή: Seminars in arthritis and rheumatism [Semin Arthritis Rheum] 2026 Oct; Vol. 80, pp. 152994. Date of Electronic Publication: 2026 May 09.
Τύπος έκδοσης: Journal Article; Scoping Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: W.B. Saunders Country of Publication: United States NLM ID: 1306053 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-866X (Electronic) Linking ISSN: 00490172 NLM ISO Abbreviation: Semin Arthritis Rheum Subsets: MEDLINE
Imprint Name(s): Publication: Philadelphia Pa : W.B. Saunders
Original Publication: New York, Stratton.
Ιατρικοί όροι (MeSH): Large Language Models* , Rheumatology* , Biomedical Research* , Research Design*, Humans ; Reproducibility of Results
Περίληψη: Objectives: To systematically identify and characterize original rheumatology research using large language models (LLMs), quantify reliance on closed/proprietary versus open systems, and assess reporting practices relevant to reproducibility (model versions, prompts, inference settings, and availability of code/data), in order to identify recurring reporting gaps and inform pragmatic considerations for improving transparency.
Methods: We searched PubMed/MEDLINE for English-language peer-reviewed original research published from 1 November 2022 to 23 January 2026. Two reviewers independently screened title/abstract and full texts, with a third resolving disagreements. Data were extracted using an LLM-assisted workflow and then independently verified against source articles by the authors. Extracted items included study characteristics, model families and openness, access mode, versioning/timing, and transparency indicators (prompts, code, data).
Results: Of 185 screened records, 63 studies were included. Most were research (n = 26/63; 41.27%) or education-focused (n = 20/63; 31.75%). Studies predominantly used closed/proprietary LLMs (n = 50/63; 79.37%), with limited exclusive use of open-weight models (n = 4/63; 6.35%) and some hybrid use (n = 9/63; 14.29%). OpenAI models were most common (n = 55/63; 87.30%). Reporting was heterogeneous: interaction language was often not reported (n = 42/63; 66.67%); access mode was reported in (n = 19/63; 30.16%); output generation date in (n = 33/63; 52.38%). Prompts were shared in (n = 48/63; 76.19%), but code was publicly available in (n = 4/63; 6.35%), and data in (n = 13/63; 20.63%).
Conclusions: LLM-based rheumatology research largely depends on closed models with inconsistent reporting of reproducibility-critical details and minimal code sharing. Clearer reporting practices and, in the longer term, consensus-based standards will be needed to strengthen the methodological robustness of this field.
(Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alfredo Madrid reports a relationship with F. Hoffmann-La Roche Ltd that includes: employment. Diego Benavent has received payment honoraria for lectures, presentations, speakers bureaus, or support for meeting attendance from AbbVie, Galapagos, Janssen, UCB, Pfizer, Novartis, support for attending meetings from UCB, Novartis and AbbVie. He works part-time as Advisor at Savana Research, company on Al in medicine.
Contributed Indexing: Keywords: Large language models; Open models; Reproducibility; Rheumatology
Entry Date(s): Date Created: 20260519 Date Completed: 20260907 Latest Revision: 20260914
Update Code: 20260915
DOI: 10.1016/j.semarthrit.2026.152994
PMID: 42156255
Βάση Δεδομένων: MEDLINE
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  Data: Large language models in rheumatology-centered research: A scoping review of model openness and reporting practices.
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  Data: <searchLink fieldCode="AU" term="%22Madrid-García+A%22">Madrid-García A</searchLink>; Independent Researcher, Spain. Electronic address: fredymad@msn.com.<br /><searchLink fieldCode="AU" term="%22Merino-Barbancho+B%22">Merino-Barbancho B</searchLink>; Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense, Madrid, Spain. Electronic address: beatriz.merino@upm.es.<br /><searchLink fieldCode="AU" term="%22Freites-Núñez+D%22">Freites-Núñez D</searchLink>; Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain.<br /><searchLink fieldCode="AU" term="%22Benavent+D%22">Benavent D</searchLink>; Rheumatology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
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  Data: Objectives: To systematically identify and characterize original rheumatology research using large language models (LLMs), quantify reliance on closed/proprietary versus open systems, and assess reporting practices relevant to reproducibility (model versions, prompts, inference settings, and availability of code/data), in order to identify recurring reporting gaps and inform pragmatic considerations for improving transparency.<br />Methods: We searched PubMed/MEDLINE for English-language peer-reviewed original research published from 1 November 2022 to 23 January 2026. Two reviewers independently screened title/abstract and full texts, with a third resolving disagreements. Data were extracted using an LLM-assisted workflow and then independently verified against source articles by the authors. Extracted items included study characteristics, model families and openness, access mode, versioning/timing, and transparency indicators (prompts, code, data).<br />Results: Of 185 screened records, 63 studies were included. Most were research (n = 26/63; 41.27%) or education-focused (n = 20/63; 31.75%). Studies predominantly used closed/proprietary LLMs (n = 50/63; 79.37%), with limited exclusive use of open-weight models (n = 4/63; 6.35%) and some hybrid use (n = 9/63; 14.29%). OpenAI models were most common (n = 55/63; 87.30%). Reporting was heterogeneous: interaction language was often not reported (n = 42/63; 66.67%); access mode was reported in (n = 19/63; 30.16%); output generation date in (n = 33/63; 52.38%). Prompts were shared in (n = 48/63; 76.19%), but code was publicly available in (n = 4/63; 6.35%), and data in (n = 13/63; 20.63%).<br />Conclusions: LLM-based rheumatology research largely depends on closed models with inconsistent reporting of reproducibility-critical details and minimal code sharing. Clearer reporting practices and, in the longer term, consensus-based standards will be needed to strengthen the methodological robustness of this field.<br /> (Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved.)
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  Data: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alfredo Madrid reports a relationship with F. Hoffmann-La Roche Ltd that includes: employment. Diego Benavent has received payment honoraria for lectures, presentations, speakers bureaus, or support for meeting attendance from AbbVie, Galapagos, Janssen, UCB, Pfizer, Novartis, support for attending meetings from UCB, Novartis and AbbVie. He works part-time as Advisor at Savana Research, company on Al in medicine.
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