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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Ultrasonography01:17

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...

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Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Artificial Intelligence in Musculoskeletal Imaging: Innovations and Clinical Impact in Rheumatology.

Reece Blay1, Amanda E Nelson2

  • 1Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC, USA.

Rheumatic Diseases Clinics of North America
|July 6, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in diagnosing and classifying rheumatic diseases from medical images. Challenges remain in validation and integration, but AI may soon aid clinicians in diagnosis and monitoring.

Keywords:
Artificial intelligenceDeep learningImagingInflammatory arthritisMachine learningOsteoarthritis

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Area of Science:

  • Rheumatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Rheumatic and musculoskeletal diseases (RMDs) pose diagnostic and management challenges.
  • Advancements in artificial intelligence (AI) offer new potential for medical imaging analysis.
  • Recent AI developments since 2020 focus on specific RMDs like rheumatoid arthritis, psoriatic arthritis, spondyloarthritis, and osteoarthritis.

Purpose of the Study:

  • To summarize key advancements in AI for RMD imaging since 2020.
  • To highlight AI applications in diagnosis, classification, and predictive modeling for RMDs.
  • To identify current challenges and future potential of AI in rheumatology imaging.

Main Methods:

  • Literature review of AI applications in RMD imaging.
  • Focus on studies published since 2020.
  • Analysis of AI's role in diagnosis, classification, and prediction of RMDs.

Main Results:

  • AI applications are emerging for diagnosis, severity classification, and prediction of RMD incidence and progression.
  • Key RMDs analyzed include rheumatoid arthritis, psoriatic arthritis, spondyloarthritis, and osteoarthritis.
  • Ongoing challenges include external validation, bias mitigation, data privacy, transparency, and clinical integration.

Conclusions:

  • AI holds potential to assist clinicians in diagnostic interpretation and disease monitoring in the near term.
  • Future AI applications may enhance prognostic modeling and identify candidates for targeted interventions and clinical trials.
  • Addressing current challenges is crucial for successful clinical integration of AI in rheumatology imaging.