Exploring the Role of Artificial Intelligence in Modern Rheumatology

Artificial intelligence (AI) is rapidly transforming many sectors, including healthcare, by enabling machines to learn from data, recognize patterns, and make informed decisions. In rheumatology, AI's integration is showing promising advances across diagnosis, disease monitoring, risk prediction, and patient communication. During the 2025 European Congress of Rheumatology (EULAR) in Barcelona, researchers presented various studies illustrating these technological innovations.

One significant application of AI is in imaging. High-resolution computed tomography (HRCT) remains the gold standard for diagnosing and evaluating the progression of interstitial lung disease (ILD), often associated with systemic sclerosis (SSc). Utilizing AI-assisted interpretation allows for more precise quantification and characterization of SSc-ILD, potentially enhancing disease monitoring. A study by Francesca Motta demonstrated that AI analysis surpasses visual scoring by radiologists in detecting fibrotic changes and correlates better with pulmonary function tests, thus enabling earlier detection of subtle disease progression.

In addition to lung imaging, AI models are being developed to improve diagnosis. Seulkee Lee's research integrated inflammatory and structural changes in sacrum MRI scans using deep learning, effectively identifying axial spondyloarthritis with high accuracy. Such models can detect features beyond traditional criteria, even identifying cases that meet clinical but not imaging standards, supporting earlier and more accurate diagnosis.

AI's capabilities extend into ultrasound imaging as well. A study led by Claus Juergen Bauer evaluated a supervised deep learning model trained on thousands of images to classify lesions characteristic of giant cell arteritis. The model demonstrated stronger diagnostic performance than conventional methods, especially in larger arteries, with ongoing efforts to improve detection in smaller vessels.

Risk prediction is another fertile ground for AI in rheumatology. Antonio Tonutti presented machine learning models that analyze clinical and serological data to predict cancer risks in patients with SSc. The models effectively identified key risk factors such as interstitial lung disease, digital ulcers, and esophageal involvement, offering personalized screening strategies that could lead to earlier cancer detection.

Remote assessment and monitoring are also seeing benefits from AI. Digital biomarkers developed through deep learning analyze hand motion via smartphone cameras, providing objective metrics of disease activity in rheumatoid arthritis. These innovative tools could facilitate telemedicine, enabling patients to be monitored remotely with accuracy comparable to in-clinic evaluations.

Furthermore, AI-powered social robots are emerging as tools to assist in patient communication. Such robots leverage natural language processing to answer common questions, enhance health literacy, and support routine follow-ups. While well-received, experts emphasize the importance of human interaction for empathetic care.

These advancements represent just a portion of AI's potential in rheumatology, promising more precise diagnostics, personalized treatments, and efficient patient engagement. As research continues, AI tools may soon become integral components of rheumatology practice, improving patient outcomes worldwide.

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