Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

• Ages Any age
• All sexes
• No healthy volunteers
• Child, Adult, Older adult

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.

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

About this study

Official title: Impact of Generative Artificial Intelligence on Diagnosing Rheumatoid Arthritis Complications

Conditions

Rheumatoid Arthritis (RA, Osteoporosis, Osteoarthritis, Interstitial Lung Disease, Thyroid Diseases, Cardiovascular Diseases

Being tested

Generative AI prediction report for RA complications

Keywords

Rheumatoid Arthritis, generative AI, large language model, Rheumatoid arthritis complications

Medical subject headings

Arthritis, Rheumatoid, Osteoporosis, Osteoarthritis, Lung Diseases, Interstitial, Thyroid Diseases, Cardiovascular Diseases, Sjogren's Syndrome, Digestive System Diseases

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

• Generative AI prediction report for RA complicationsOther

  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 being measured

The primary outcome is the main question the study is designed to answer.

1. Will physicians adopt GenAI predictions in diagnosing RA complications?

   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.

   Measured: Immediately after reviewing patient AI report on the day of admission.

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

1. First posted24 Dec 2025
2. Study start1 Oct 2025
3. Primary completionFeb 2026 (estimated)
4. Study completionJun 2026 (estimated)

Resources

• Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
• Other trials for Rheumatoid Arthritis (RAStudies of the same condition, open and under way.
• What the trial phases meanHow a treatment moves from first tests in people to approval.

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.