Objective:

To systematically review the application of artificial intelligence (AI) in the diagnosis and management of gout, highlighting current challenges and future research directions.

Approach:

• AI Overview: AI utilizes algorithms and statistical models to perform tasks requiring human intelligence, including machine learning and deep learning, which can analyze large datasets for gout diagnosis and treatment.
• Data Mining in Gout: NLP can enhance the identification of gout flares in medical databases by processing unstructured electronic health record data.

Key Findings:

• Gout prevalence is rising globally, with 55.8 million people affected in 2020, an increase of 22.5% since 1990.
• Traditional diagnostic methods for gout have limitations, including the difficulty in distinguishing gout from other types of arthritis.
• AI shows promise in enhancing diagnosis, treatment planning, and disease monitoring in gout care.

Interpretation:

Limitations:

• Current AI applications in gout are still in developmental stages.
• Challenges remain in accurately identifying gout flares due to the transient nature of symptoms.

Conclusion:

Further research is needed to fully realize the benefits of AI in gout diagnosis and management.