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Editorial
. 2025 Feb;28(2):e70124.
doi: 10.1111/1756-185X.70124.

Harnessing Large Language Models for Rheumatic Disease Diagnosis: Advancing Hybrid Care and Task Shifting

Fabian Lechner  1   2 , Sebastian Kuhn  1 , Johannes Knitza  1
Affiliations
Editorial

Harnessing Large Language Models for Rheumatic Disease Diagnosis: Advancing Hybrid Care and Task Shifting

Fabian Lechner et al. Int J Rheum Dis. 2025 Feb.
No abstract available

Keywords: artificial intelligence; diagnosis; hybrid care; large language models; task shifting.

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References

    1. E. M. Ruderman and A. Ogdie, “Addressing the Rheumatology Workforce Shortage: A Question of Supply and Demand,” Journal of Rheumatology 49, no. 11 (2022):1183–1184, https://doi.org/10.3899/jrheum.220709.
    1. F. Fuchs, H. Morf, J. Mohn, et al., “Diagnostic Delay Stages and Pre‐Diagnostic Treatment in Patients With Suspected Rheumatic Diseases Before Special Care Consultation: Results of a Multicenter‐Based Study,” Rheumatology International 43, no. 3 (2022): 495–502, https://doi.org/10.1007/s00296‐022‐05223‐z.
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    1. J. Knitza, L. Gupta, and T. Hügle, “Rheumatology in the Digital Health Era: Status Quo and Quo Vadis?,” Nature Reviews Rheumatology 20, no. 12 (2024): 747–759, https://doi.org/10.1038/s41584‐024‐01177‐7.
    1. M. C. Van Schalkwyk, A. Bourek, D. S. Kringos, et al., “The Best Person (Or Machine) for the Job: Rethinking Task Shifting in Healthcare,” Health Policy 124, no. 12 (2020): 1379–1386, https://doi.org/10.1016/j.healthpol.2020.08.008.

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