Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis

December 22, 2025 updated by: Quan Jiang, Guang'anmen Hospital of China Academy of Chinese Medical Sciences

Impact of Generative Artificial Intelligence on Diagnosing Rheumatoid Arthritis Complications

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Quan Jiang Guang'anmen Hospital, China Academy of Chinese Medical Science
  • Phone Number: 010-88001942
  • Email: doctorjq@126.com

Study Locations

  • China
    • Beijing Municipality
      • Beijing, Beijing Municipality, China, 100053
        • Recruiting
        • Guang'anmen Hospital of China Academy of Chinese Medical Sciences
        • Contact:
        • Principal Investigator:
          • Quan Jiang, MD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Adult male and female RA inpatients admitted to our Rheumatology Department who fulfill the 2010 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification and diagnostic criteria for rheumatoid arthritis.

Description

Inclusion Criteria:

  • 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 Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Other: Generative 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Will physicians adopt GenAI predictions in diagnosing RA complications?
Time Frame: Immediately after reviewing patient AI report on the day of admission.
In the routine care workflow, large language models (LLMs) are used to predict potential RA complications for each de-identified patient case and generate an AI report listing possible complications and co-existing diseases. Additional diagnostic tests are suggested to verify the predicted conditions. After reviewing the AI report, physicians immediately evaluate each disease prediction using a 5-point Likert scale (1 = complete disagreement; 2 = disagreement; 3 = neutral; 4 = agreement; 5 = complete agreement). The mean score is calculated as a measure of perceived prediction accuracy. Physicians also indicate whether each specific disease prediction could potentially be adopted or used to assist differential diagnosis (binary: 0 or 1). The percentage of positive adoption responses is calculated as a measure of potential adoption rate, or adoptability.
Immediately after reviewing patient AI report on the day of admission.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
To what extent are RA complication diagnoses actually affected by GenAI predictions?
Time Frame: Immediately after making the final diagnosis at discharge.
Before patient discharge, physicians make final diagnoses and record which diagnosed complications or co-existing diseases were influenced by GenAI prediction information for each patient. The percentage of cases in which GenAI predictions affected the final diagnosis is calculated as a measure of AI's actual impact on routine diagnostic practice.
Immediately after making the final diagnosis at discharge.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Guang'anmen Hospital of China Academy of Chinese Medical Sciences

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2025

Primary Completion (Estimated)

February 1, 2026

Study Completion (Estimated)

June 1, 2026

Study Registration Dates

First Submitted

September 28, 2025

First Submitted That Met QC Criteria

December 22, 2025

First Posted (Actual)

December 24, 2025

Study Record Updates

Last Update Posted (Actual)

December 24, 2025

Last Update Submitted That Met QC Criteria

December 22, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2025-201-KY

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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