What does artificial intelligence mean in rheumatology?

Area of Science:

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Deep Learning (DL)
• Large Language Models (LLMs)

Background:

• AI involves transferring human-like learning abilities to computers.
• Machine learning enables intelligent data understanding, while deep learning utilizes neural networks for image/video analysis.
• LLMs represent a recent AI advancement integrating self-learning with deep learning via transformers.

Purpose of the Study:

• To explore the transformative potential of AI in rheumatology research and healthcare.
• To highlight AI applications in clinical decision-making, diagnostics, and predictive analytics.
• To discuss AI's role in analyzing complex biological data and automating administrative tasks.

Main Methods:

• Utilizing machine learning for clinical diagnosis and decision support.
• Employing deep learning for analyzing radiological, PET, and histopathology images.
• Leveraging AI for predictive modeling of disease flares using patient data and wearables.
• Analyzing high-volume omics data (genomics, transcriptomics, proteomics, metabolomics) for prognostic markers and therapeutic targets.

Main Results:

• AI can significantly aid clinical diagnosis and decision-making processes.
• Deep learning enhances diagnostic accuracy through medical image analysis.
• AI facilitates prediction of disease flares and identification of novel prognostic markers.
• AI shows potential in discovering new therapeutic targets and automating administrative tasks.

Conclusions:

• AI, encompassing ML, DL, and LLMs, is poised to revolutionize rheumatology.
• AI applications range from enhanced diagnostics and personalized treatment to administrative efficiency.
• Ethical considerations regarding AI misuse are crucial, but its integration into rheumatology is inevitable and promising.