Harnessing Large Language Models for Rheumatic Disease Diagnosis: Advancing Hybrid Care and Task Shifting
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Medical domain-specific tools increasingly use LLMs to retrieve verified medical information and, through retrieval-augmented generation, translate this validated content into personalized answers rather than relying solely on training data.
“Medical domain-specific tools increasingly use LLMs to retrieve verified medical information and, through retrieval-augmented generation (RAG), translate this validated content into personalized answers, rather than relying solely on training data [8].”
From Diagnostic performance of Prof. Valmed, ChatGPT-5 Thinking, and OpenEvidence in rheumatology: A comparative evaluation (2026) · cites this paper as [8]
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Cites
Joint effect of ageing and multilayer structure prevents ordering in the voter model
Artime, O., Fernández-Gracia, J., Ramasco, J. J., & Miguel, M. S. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.48550/arxiv.1703.06073 · Cited by 5,608 · see citing works
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
Almanac — Retrieval-Augmented Language Models for Clinical Medicine
Zakka, C., Shad, R., Chaurasia, A., Dalal, A. R., Kim, J. L., Moor, M., Fong, R., Phillips, C., Alexander, K., Ashley, E., et al. (2024) · NEJM AI · doi:10.1056/aioa2300068 · Cited by 424 · see citing works
Med-HALT: Medical Domain Hallucination Test for Large Language Models
Pal, A., Umapathi, L. K., & Sankarasubbu, M. (2023) · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/2023.conll-1.21 · Cited by 152 · see citing works
Med-HALT: Medical Domain Hallucination Test for Large Language Models
Pal, A., Umapathi, L. K., & Sankarasubbu, M. (2023) · arXiv · doi:10.48550/arxiv.2307.15343 · Cited by 152 · see citing works
Time Spent on Clinical Documentation
Oxentenko, A. S. (2010) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.534 · Cited by 139 · see citing works
Capabilities of Gemini Models in Medicine
Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.18416 · Cited by 117 · see citing works
Diagnostic accuracy of a large language model in rheumatology: comparison of physician and ChatGPT-4
Krusche, M., Callhoff, J., Knitza, J., & Ruffer, N. (2023) · Rheumatology International · doi:10.1007/s00296-023-05464-6 · Cited by 116 · see citing works
Self-citationTowards Conversational Diagnostic AIPreprint
Tu, T., Palepu, A. K., Schaekermann, M., Saab, K. K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Tomašev, N., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.05654 · Cited by 114 · see citing works
Digital rheumatology in the era of COVID-19: results of a national patient and physician survey
Kernder, A. L., Morf, H., Klemm, P., Vossen, D., Haase, I., Mücke, J., Meyer, M., Kleyer, A., Sewerin, P., Bendzuck, G., et al. (2021) · RMD Open · doi:10.1136/rmdopen-2020-001548 · Cited by 103 · see citing works
Self-citationThe best person (or machine) for the job: Rethinking task shifting in healthcare
van Schalkwyk, M. C., Bourek, A., Kringos, D. S., Siciliani, L., Barry, M. M., Maeseneer, J. D., & McKee, M. (2020) · Health Policy · doi:10.1016/j.healthpol.2020.08.008 · Cited by 98 · see citing works
Madrid-García, A., Rosales, Z., Freites, D., Pérez-Sancristóbal, I., Pato-Cour, E., Plasencia‐Rodríguez, C., Osorio, L. C., Abásolo, L., León, L., Fernández‐Gutiérrez, B., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-49483-6 · Cited by 77 · see citing works
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Knitza, J., Gupta, L., & Hügle, T. (2024) · Nature Reviews Rheumatology · doi:10.1038/s41584-024-01177-7 · Cited by 66 · see citing works
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Alder, H., Michel, B. A., Marx, C., Tamborrini, G., Langenegger, T., Bruehlmann, P., Steurer, J., & Wildi, L. M. (2014) · International Journal of Rheumatology · doi:10.1155/2014/672714 · Cited by 47 · see citing works
Fuchs, F., Morf, H., Mohn, J., Mühlensiepen, F., Ignatyev, Y., Bohr, D., Araujo, E. G., Bergmann, C., Simón, D., Kleyer, A., et al. (2022) · Rheumatology International · doi:10.1007/s00296-022-05223-z · Cited by 36 · see citing works
Self-citationA hybrid framework with large language models for rare disease phenotyping
Wu, J., Dong, H., Li, Z., Wang, H., Li, R., Patra, A., Dai, C., Ali, W., Scordis, P., & Wu, H. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02698-7 · Cited by 34 · see citing works
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Haase, I., Xiong, T., Rissmann, A., Knitza, J., Greenfield, J., & Krusche, M. (2024) · The Lancet Rheumatology · doi:10.1016/s2665-9913(24)00056-0 · Cited by 28 · see citing works
Self-citationLarge language model-driven sentiment analysis for facilitating fibromyalgia diagnosis
Venerito, V., & Iannone, F. (2024) · RMD Open · doi:10.1136/rmdopen-2024-004367 · Cited by 28 · see citing works
Computer-assisted diagnosis of rheumatic disorders
Moens, H. J. B., & Korst, J. K. V. D. (1991) · Seminars in Arthritis and Rheumatism · doi:10.1016/0049-0172(91)90004-j · Cited by 18 · see citing works
Digitale Diagnoseunterstützung in der Rheumatologie
Knitza, J., Krusche, M., & Leipe, J. (2021) · Zeitschrift für Rheumatologie · doi:10.1007/s00393-021-01097-x · Cited by 8 · see citing works
Self-citationAddressing the Rheumatology Workforce Shortage: A Question of Supply and Demand
Ruderman, E., & Ogdie, A. (2022) · The Journal of Rheumatology · doi:10.3899/jrheum.220709 · Cited by 4 · see citing works
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Research works printed but not linked: 1
- A. Vaswani, N. Shazeer, N. Parmar, et al., "Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS'17. Curran Associates Inc," 2017, 6000-6010.
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Labinsky, H., Klemm, P., Graalmann, L., Thiele, T., Hornig, J., Fink, D., Morf, H., Mucke, J., Kiltz, U., Pecher, A.-C., et al. (2025) · Rheumatology International · doi:10.1007/s00296-025-06023-x · Cited by 20 · see citing works
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“Medical domain-specific tools increas - ingly use LLMs to retrieve verified medical information and, through retrieval-augmented generation (RAG), translate this validated content into personalized answers, rather than relying solely on training data [8].”
Self-citationLumma, F., Knitza, J., & Muehlensiepen, F. (2025) · Rheumatology International · doi:10.1007/s00296-025-05893-5 · Cited by 3 · see citing works
Self-citationFelde, S., Buchkremer, R., Chehab, G., Thielscher, C., Distler, J. H. W., Schneider, M., & Richter, J. G. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1817215 · Cited by 2 · see citing works
Large Language Models in der Rheumatologie
Knitza, J., & Hügle, T. (2026) · Zeitschrift für Rheumatologie · doi:10.1007/s00393-026-01803-7 · Cited by 1 · see citing works
Self-citationKnitza, J., Mücke, J., Muehlensiepen, F., Xiong, T., Grahammer, M., Greenfield, J., Kühn, S., Vuillerme, N., Krönke, G., Schett, G. A., et al. (2025) · EULAR Rheumatology Open · doi:10.1016/j.ero.2025.06.004
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Kühn, S., & Knitza, J. (2025) · Osteologie/Osteology · doi:10.1055/a-2697-4943
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Su, S., Shen, W.-C., Kuo, S., & Lin, H. (2026) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.70568
Schulz, N., Gehlen, M., Mahn, K., Bühring, B., Hoff, P., Seifert, O., Wiebe, E., Rashid, A., Kuhn, S., Buttgereit, F., et al. (2026) · Osteoporosis International · doi:10.1007/s00198-026-08247-4
Self-citationWilhelmi, T., Bartsch, V., Platt, M., Rashid, A., Hornig, J., Krusche, M., Hueber, A. J., Fink, D., Pfeil, A., Dischereit, G., et al. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02470-6
Self-citationRoemer, A., Wilhelmi, T., Fink, D., Göbel, M., Hack, J., Bamberger, J., Mühlensiepen, F., Gente, K., Kuhn, S., Klemm, P., et al. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-15808-0
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- Authors
- Lechner, F., Kuhn, S., & Knitza, J.
- Affiliations
- Philipps University of Marburg (DE)
- Corresponding author
- Knitza, Johannes
- Venue
- International Journal of Rheumatic Diseases
- Publisher
- Wiley
- Published
- 2025-02
- Volume
- 28
- Issue
- 2
- Pages
- e70124-e70124
- ISSN
- 1756-1841 (ISSN-L)
- 1756-185X
- OpenAlex
- W4407226919
- Cited by
- 11
- References
- 20
- Citations by year
- 2026: 7 · 2025: 4
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Rheumatic Diseases (publishedVersion)
- PubMed (publishedVersion)
- 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 › Rehabilitation › Musculoskeletal Disorders and Rehabilitation
- Keywords
- large language models, hybrid care, task shifting, clinical decision support, artificial intelligence in healthcare
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