RecruitingNCT07301892

Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis

Impact of Generative Artificial Intelligence on Diagnosing Rheumatoid Arthritis Complications


Sponsor

Guang'anmen Hospital of China Academy of Chinese Medical Sciences

Enrollment

100 participants

Start Date

Oct 1, 2025

Study Type

OBSERVATIONAL

Conditions

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

Inclusion Criteria3

  • 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.

Exclusion Criteria1

  • Patients subsequently confirmed not to have RA during the study.

Interventions

OTHERGenerative AI prediction report for RA complications

Generative AI based on multiple large language models (LLMs) is used to predict potential complications and co-existing diseases in patients with rheumatoid arthritis using EHR data available at admission. Physicians use these AI predictions as additional information to adjust their diagnostic plans during differential diagnosis. The impact of this intervention on the final diagnoses at discharge will be measured. Before the prospective study, the adoptability of the generative AI prediction reports will be validated using EHR records from retrospective RA patients.


Locations(1)

Guang'anmen Hospital of China Academy of Chinese Medical Sciences

Beijing, Beijing Municipality, China

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NCT07301892


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