Zhao J, Li L, Li J, Zhang L. Application of artificial intelligence in rheumatic disease: a bibliometric analysis. Clin Exp Med. 2024;24(1):196. https://doi.org/10.1007/s10238-024-01453-6.
Article PubMed PubMed Central Google Scholar
Wang J, Tian Y, Zhou T, Tong D, Ma J, Li J. A survey of artificial intelligence in rheumatoid arthritis. Rheumatol Immunol Res. 2023;4:69–77. https://doi.org/10.2478/rir-2023-0011.
Article PubMed PubMed Central Google Scholar
Momtazmanesh S, Nowroozi A, Rezaei N. Artificial intelligence in rheumatoid arthritis: current status and future perspectives: a state-of‐the‐art review. Rheumatol Ther. 2022;9(5):1249–304. https://doi.org/10.1007/s40744-022-00475-4.
Article PubMed PubMed Central Google Scholar
Gilvaz VJ, Reginato AM. Artificial intelligence in rheumatoid arthritis: potential applications and future implications. Front Med (Lausanne). 2023;10:1280312. https://doi.org/10.3389/fmed.2023.1280312.
U.S. Food and Drug Administration. Artificial intelligence and machine learning (AI/ML)–enabled medical devices [Internet]. Silver Spring (MD): U.S. Food and Drug Administration; 2025 [cited 2025 May 10]. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
Kow J, Tan YK. An update on thermal imaging in rheumatoid arthritis. Joint Bone Spine. 2023;90(3):105496. https://doi.org/10.1016/j.jbspin.2022.105496.
Ahalya RK, Almutairi FM, Snekhalatha U, Dhanraj V, Aslam SM. RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images. Sci Rep. 2023;13(1):15638. https://doi.org/10.1038/s41598-023-42111-3.
Article CAS PubMed PubMed Central Google Scholar
Kesavapillai AR, Aslam SM, Umapathy S, Almutairi F. RA-XTNet: a novel CNN model to predict rheumatoid arthritis from hand radiographs and thermal images: a comparison with CNN transformer and quantum computing. Diagnostics (Basel). 2024;14(17):1911. https://doi.org/10.3390/diagnostics14171911.
Cobb R, Cook GJR, Reader AJ. Deep learned segmentations of inflammation for novel 99mTc-maraciclatide imaging of rheumatoid arthritis. Diagnostics (Basel). 2023;13(21):3298. https://doi.org/10.3390/diagnostics13213298.
Article CAS PubMed Google Scholar
Salomon-Escoto K, Kay J. The treat to target approach to rheumatoid arthritis. Rheum Dis Clin North Am. 2019;45(4):487–504. https://doi.org/10.1016/j.rdc.2019.06.001.
Guan Y, Zhang H, Quang D, Wang Z, Parker SCJ, Pappas DA, et al. Machine learning to predict anti–tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis Rheumatol. 2019;71:1987–96. https://doi.org/10.1002/art.41056.
Article CAS PubMed Google Scholar
Prasad B, Siqueira NF, Mukherjee R, et al. ATRPred: a machine learning–based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients. PLoS Comput Biol. 2022;18(7):e1010204. https://doi.org/10.1371/journal.pcbi.1010204.
Article CAS PubMed PubMed Central Google Scholar
Chen S, Walter P, Wei JC et al. Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms. In: Proceedings of the 2022 9th International Conference on Biomedical and Bioinformatics Engineering. 2022. https://doi.org/10.1145/3574198.357422
Kalweit M, Jørgensen TS, Hansen NS, et al. Patient groups in rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs. PLoS Comput Biol. 2023;19(6):e1011073. https://doi.org/10.1371/journal.pcbi.1011073.
Article CAS PubMed PubMed Central Google Scholar
Stroud L, Murchison A, Raje A, Lehman C. How accurate and comprehensible is ChatGPT for patients regarding breast cancer prevention and screening? Breast Cancer Res Treat. 2024;197(1):35–43. https://doi.org/10.1007/s10549-023-07012-2.
Knitza J, Tascilar K, Fuchs F, et al. Diagnostic accuracy of a mobile AI-based symptom checker and a web-based self-referral tool in rheumatology: multicenter randomized controlled trial. J Med Internet Res. 2024;26:e55542. https://doi.org/10.2196/55542.
Article PubMed PubMed Central Google Scholar
Coskun BN, Yagiz B, Ocakoglu G, Dalkilic E, Pehlivan Y. Assessing the accuracy and completeness of artificial intelligence Language models in providing information on methotrexate use. Rheumatol Int. 2024;44:509–15. https://doi.org/10.1007/s00296-023-05473-5.
Chen CW, Walter P, Wei JC. Using ChatGPT-like solutions to Bridge the communication gap between patients with rheumatoid arthritis and health care professionals. JMIR Med Educ. 2024;10:e48989. https://doi.org/10.2196/48989.
Article PubMed PubMed Central Google Scholar
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with alphafold. Nature. 2021;596(7873):583–9. https://doi.org/10.1038/s41586-021-03819-2.
Article CAS PubMed PubMed Central Google Scholar
Nabi T, Rathore P, Dinakar P, et al. Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of rheumatoid arthritis. PLoS ONE. 2024;19(12):e0303954. https://doi.org/10.1371/journal.pone.0303954.
Article CAS PubMed PubMed Central Google Scholar
Wu YK, Zhang L, Li P, et al. Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis. Front Immunol. 2024;15:1387311. https://doi.org/10.3389/fimmu.2024.1387311.
Article CAS PubMed PubMed Central Google Scholar
Lim AJW, Chuah YJ, Lim WL, et al. Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score. J Transl Med. 2023;21:92. https://doi.org/10.1186/s12967-023-03939-5.
Article CAS PubMed PubMed Central Google Scholar
Warraich HJ, Tazbaz T, Califf RM. FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA. 2024;333(3):241–7. https://doi.org/10.1001/jama.2024.21451.
Joshi G, Kumar A, Singh R, et al. FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape. Electronics. 2024;13(3):498. https://doi.org/10.3390/electronics13030498.
Balogun E, Ajayi A, Olojede S, et al. Exploring key stakeholders’ perspectives on integrating the EU AI act with the MDR for certifying AI medical devices. AI Ethics. 2024. https://doi.org/10.1007/s43681-024-00612-5.
Haberle T, Zhang Y, Stockbridge N, et al. The impact of nuance DAX ambient listening AI documentation: a cohort study. J Am Med Inf Assoc. 2024;31(4):975–9. https://doi.org/10.1093/jamia/ocae022.
Ratwani RM, Sutton K, Galarraga JE. Addressing AI algorithmic bias in health care. JAMA. 2024;332(13):1051–2. https://doi.org/10.1001/jama.2024.13486.
Chelli M, Cornet V, Illanes-Avila M, et al. Hallucination rates and reference accuracy of ChatGPT and bard for systematic reviews: comparative analysis. J Med Internet Res. 2024;26:e53164. https://doi.org/10.2196/53164.
Article PubMed PubMed Central Google Scholar
Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–15. https://doi.org/10.1038/s42256-019-0048-X.
Article PubMed PubMed Central Google Scholar
Williamson SM, Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci. 2024;14(2):675. https://doi.org/10.3390/app14020675.
Acharya DB, Kuppan K, Divya B. Agentic AI: autonomous intelligence for complex goals– a comprehensive survey. IEEE Access. 2025.
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