AI Medical Compendium Journal:
Clinical rheumatology

Showing 1 to 10 of 10 articles

Assessment of ChatGPT's adherence to EULAR diagnostic criteria and therapeutic protocols for rheumatoid arthritis at two distinct time points, 14 days apart, utilizing binary and multiple-choice inquiries.

Clinical rheumatology
OBJECTIVES: Artificial intelligence (AI) possesses considerable promise in healthcare to offer decision help in particular domains, including rheumatoid arthritis (RA). This study assesses the adherence of the advanced AI model ChatGPT-v4 to the Euro...

Review of 2024 publications on the applications of artificial intelligence in rheumatology.

Clinical rheumatology
The integration of artificial intelligence (AI) into rheumatology has revolutionized research and clinical practice, offering transformative advancements in diagnostics, biomarker discovery, genomics, digital health technologies, and personalized med...

Machine learning-based prediction model integrating ultrasound scores and clinical features for the progression to rheumatoid arthritis in patients with undifferentiated arthritis.

Clinical rheumatology
OBJECTIVES: Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches combining clinical assessments and ultrasonography (US) often lack accuracy due to the complex intera...

Comparative performance of artificial intelligence models in rheumatology board-level questions: evaluating Google Gemini and ChatGPT-4o.

Clinical rheumatology
OBJECTIVES: This study evaluates the performance of AI models, ChatGPT-4o and Google Gemini, in answering rheumatology board-level questions, comparing their effectiveness, reliability, and applicability in clinical practice.

Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model.

Clinical rheumatology
OBJECTIVE: The clinical manifestations of systemic sclerosis (SSc) are highly variable, resulting in varied outcomes and complications. Diverse fibrosis of the skin and internal organs, vasculopathy, and dysregulated immune system lead to poor and va...

A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease.

Clinical rheumatology
INTRODUCTION: In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions.

Machine learning-based prediction of radiographic progression in patients with axial spondyloarthritis.

Clinical rheumatology
INTRODUCTION: Machine learning is applied to characterize the risk and predict outcomes in multi-dimensional data. The prediction of radiographic progression in axial spondyloarthritis (axSpA) remains limited. Hence, we tested the feasibility of supe...

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Clinical rheumatology
OBJECTIVE: To develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.

Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network.

Clinical rheumatology
INTRODUCTION: Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and...