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Emerging Artificial Intelligence Innovations in Rheumatoid Arthritis and Challenges to Clinical Adoption.

Vinit J Gilvaz1, Aishwarya Sudheer2, Anthony M Reginato3,4

  • 1Division of Rheumatology, Department of Internal Medicine, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA. Vjosephgilvaz@brownhealth.org.

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This summary is machine-generated.

Artificial intelligence (AI) shows promise in rheumatoid arthritis (RA) care, aiding early detection, treatment prediction, and drug discovery. However, regulatory hurdles and implementation challenges limit clinical adoption of these advanced AI tools.

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Area of Science:

  • Rheumatology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Growing interest in AI for augmenting rheumatology care delivery.
  • Need to bridge the gap between AI innovation and clinical practice.

Purpose of the Study:

  • Inform rheumatologists about recent AI research in rheumatoid arthritis (RA).
  • Highlight AI developments from 2023-2025 in diagnostics, treatment, drug discovery, and patient tools.

Main Methods:

  • Review of recent AI-based research in rheumatoid arthritis (RA).
  • Focus on developments in diagnostic imaging, treatment prediction, drug discovery, and patient-facing tools.
  • Analysis of AI model accuracy and potential clinical applications.

Main Results:

  • AI models show high accuracy in early RA detection via imaging.
  • Predictive models using EHR data are nearing clinical application.
  • Patient-facing AI tools show potential but have variable accuracy/safety.
  • AI aids biomarker identification and accelerates drug discovery.
  • No AI tools approved by FDA for rheumatology use as of early 2025.

Conclusions:

  • AI offers significant promise for enhancing RA care, from diagnosis to personalized therapy.
  • Clinical adoption is hindered by regulatory, technical, and implementation challenges.
  • Streamlined regulation and collaboration are crucial for AI integration in rheumatology.
  • AI can be a valuable adjunct for managing clinical complexity and workforce shortages.