The Use of Artificial Intelligence in Improving Diagnostic Modalities in Rheumatoid Arthritis: A Narrative Review

Area of Science:

• Rheumatology
• Medical Artificial Intelligence
• Autoimmune Diseases

Background:

• Rheumatoid arthritis (RA) is a complex autoimmune condition impacting joints and organs.
• Current RA diagnosis relies on clinical assessment, biomarkers, and imaging, facing challenges like false negatives and delayed detection.
• Early and accurate diagnosis is crucial to prevent irreversible joint damage.

Purpose of the Study:

• To evaluate the impact of artificial intelligence (AI) on improving the diagnostic accuracy of rheumatoid arthritis (RA).
• To explore AI's role in early RA prediction and prevention of joint deterioration.
• To compare AI-based diagnostic approaches with traditional methods.

Main Methods:

• AI computational models utilizing machine learning (ML), natural language processing (NLP), and expert systems.
• Analysis of radiological studies (X-ray, MRI, CT, PET) for quantifying joint changes.
• Integration of ML with microRNA gene profiling for assessing joint microenvironment modulation.

Main Results:

• AI-based algorithms significantly improve diagnostic accuracy for rheumatological diseases, particularly RA.
• AI enhances early disease prediction and identification of specific disease patterns.
• ML-integrated gene profiling demonstrates superior potential in reducing joint deterioration compared to RF and CCP scoring.

Conclusions:

• AI represents a substantial advancement in RA diagnostic modalities, offering timely prediction and prevention.
• AI-based approaches overcome limitations of traditional diagnostic methods, enhancing accuracy and early detection.
• The integration of AI with advanced techniques like gene profiling holds promise for transforming RA management.