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Related Concept Videos

Autoimmune Disorders01:29

Autoimmune Disorders

Autoimmune diseases are a group of disorders in which the body's immune system mistakenly attacks its own cells, tissues, and organs. This results from an overactive immune response against substances and tissues normally present in the body. Let's delve into the concept and mechanism of autoimmune diseases from an immune system point of view, explore different causes and examples of such diseases, and discuss potential solutions.
Concept and Mechanism of Autoimmune Diseases
The immune system...
The JAK-STAT Signaling Pathway01:20

The JAK-STAT Signaling Pathway

Several cytokine receptors have tightly bound Janus kinase or JAK proteins attached at their cytosolic tail. Small signaling molecules such as cytokines, growth hormones, or prolactins bind to the cytokine receptors and initiate their dimerization. The dimerization brings the cytosolic JAKs together that trans-phosphorylate and activates each other. The activated JAKs now phosphorylate cytosolic tails of the cytokine receptors, which serve as binding sites for adaptor proteins such as  SH2...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Rheumatic Heart Disease I: Introduction01:23

Rheumatic Heart Disease I: Introduction

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Related Experiment Videos

Demystifying Artificial Intelligence: Key Concepts with Examples in Rheumatology.

Jamie E Collins1, Rayan Harari2, Jeffrey Duryea3

  • 1Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.

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

Artificial intelligence (AI) is revolutionizing rheumatology by analyzing complex data for better decision-making. This article clarifies core AI concepts and methods, making them accessible for researchers and clinicians.

Keywords:
Artificial intelligenceDeep learningLarge language modelsMachine learning

Related Experiment Videos

Area of Science:

  • Rheumatology
  • Medical Informatics
  • Computational Science

Background:

  • Artificial intelligence (AI) offers advanced computational methods for tasks requiring human intelligence.
  • AI is increasingly used in rheumatology for data analysis and clinical decision support.
  • Foundational AI concepts are often perceived as complex and inaccessible by clinicians.

Purpose of the Study:

  • To demystify foundational artificial intelligence concepts for rheumatology.
  • To define core AI terminology and explain major learning paradigms.
  • To illustrate AI applications in rheumatology with clinical examples.

Main Methods:

  • Explanation of core AI terminology.
  • Description of major AI learning paradigms (e.g., supervised, unsupervised, reinforcement learning).
  • Discussion of analytical methods used in AI, contrasting modern models with traditional approaches.

Main Results:

  • Clear definitions of fundamental AI concepts are provided.
  • Key AI learning paradigms and analytical methods are described.
  • Clinically relevant examples from rheumatology illustrate AI's practical applications.

Conclusions:

  • Demystifying AI concepts enhances its adoption in rheumatology research and practice.
  • Understanding AI principles empowers clinicians to leverage its potential for improved patient care.
  • This work bridges the gap between complex AI technology and clinical application in rheumatology.