Retrospective study demonstrates high performance in diagnosing rheumatoid arthritis, indicating potential for clinical use.
This attention-based deep learning approach shows promise as an effective, reliable, and efficient tool for the automated diagnosis of RA. The ability to achieve high performance with limited data highlights its potential for real-world clinical adoption, particularly in resource-constrained environments.
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Çetintaş et al. (2026) studied this question.
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