Rheumatoid Arthritis (RA) Clinical Trials

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Impact of Generative Artificial Intelligence on Diagnosing Rheumatoid Arthritis Complications

Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Observational
SUMMARY

Generative AI (GenAI) based on large language models (LLMs) is expected to improve the diagnosis and treatment of autoimmune diseases. We are studying how GenAI may affect the diagnosis of various complications of rheumatoid arthritis (RA). In a retrospective study using RA patients' EHR records, we will quantify physician adoption of GenAI predictions for RA complications and co-existing diseases. In a prospective observational study, we will assess the feasibility of using GenAI predictions as additional clinical information to help physicians make more complete diagnoses of RA complications and co-existing diseases, including complex, uncommon, or rare conditions.

Eligibility
Participation Requirements
Sex: All
Healthy Volunteers: f
View:

• Patients with an initial diagnosis of rheumatoid arthritis (RA).

• All real-world RA inpatients admitted to our department.

• Admission occurring within the real-world data study period.

Locations
Other Locations
China
Guang'anmen Hospital of China Academy of Chinese Medical Sciences
RECRUITING
Beijing
Contact Information
Primary
Quan Jiang Guang'anmen Hospital, China Academy of Chinese Medical Science
doctorjq@126.com
010-88001942
Time Frame
Start Date: 2025-10-01
Estimated Completion Date: 2026-06
Participants
Target number of participants: 100
Treatments
RA patient group using generative AI prediction reports
Inpatients newly diagnosed with rheumatoid arthritis in our rheumatology department between October 1, 2025, and June 2026 will be recruited for the study. Physicians will use GenAI predictions of potential RA complications and co-existing diseases, together with confirmatory diagnostic tests, as additional inputs in the differential diagnosis process.
Sponsors
Leads: Guang'anmen Hospital of China Academy of Chinese Medical Sciences

This content was sourced from clinicaltrials.gov