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  11. What Does Artificial Intelligence Mean In Rheumatology?

What does artificial intelligence mean in rheumatology?

Kunal Chandwar1, Durga Prasanna Misra1

  • 1Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India.

Archives of Rheumatology
|May 22, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI), including machine learning and deep learning, offers revolutionary potential in rheumatology for diagnosis, prognosis, and treatment discovery. While ethical challenges exist, AI

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.
Keywords:
Artificial intelligencedeep learningimage analysis machine learningrheumatology

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  • 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.