Rheumatoid Arthritis (RANCT07301892
Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis
An observational study of Rheumatoid Arthritis (RA and Osteoporosis, run by Guang'anmen Hospital of China Academy of Chinese Medical Sciences at 1 site in China.
Trial 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). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Rheumatoid Arthritis (RA, Osteoporosis, Osteoarthritis
- What is being tested?
- Generative AI prediction report for RA complications
- How many people
- 57 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2025to 28 Feb 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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.
Who may be able to join
- 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.
Who cannot join
- Patients subsequently confirmed not to have RA during the study.
Trial locations and contacts
1 site in 1 country.
China1 site
Guang'anmen Hospital of China Academy of Chinese Medical Sciences
Beijing, Beijing Municipality, 100053
About this study
Official title: 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.
- 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Generative AI prediction report for RA complications |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Secondary outcomes (1)
To what extent are RA complication diagnoses actually affected by GenAI predictions?
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.
Measured: Immediately after making the final diagnosis at discharge.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Dec 2025
- Study start1 Oct 2025
- Primary completion31 Jan 2026
- Study completion28 Feb 2026
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.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 57 participants (actual)
Sponsor and investigators
- Lead sponsor
- Guang'anmen Hospital of China Academy of Chinese Medical Sciences
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Quan Jiang, Guang'anmen Hospital of China Academy of Chinese Medical Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07301892
- Sponsor's protocol ID
- 2025-201-KY
- Status last verified
- Oct 2026
- First submitted
- 28 Sep 2025
- Last updated
- 26 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact of Generative Artificial Intelligence on Diagnosing Rheumatoid Arthritis Complications
- ClinicalTrials.gov ID
- NCT07301892
- Acronym / sponsor ID
- 2025-201-KY
- Conditions
- Rheumatoid Arthritis (RA, Osteoporosis, Osteoarthritis, Interstitial Lung Disease, Thyroid Diseases, Cardiovascular Diseases
- Keywords
- Rheumatoid Arthritis, generative AI, large language model, Rheumatoid arthritis complications
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 57 participants (actual)
- Lead sponsor
- Guang'anmen Hospital of China Academy of Chinese Medical SciencesUniversity, hospital or other
- Study start
- 1 Oct 2025 (actual)
- Primary completion
- 31 Jan 2026 (actual)
- Study completion
- 28 Feb 2026 (actual)
- First posted
- 24 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| RA patient group using generative AI prediction reports | – | Other: Generative AI prediction report for RA complicationsInpatients 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (1 listed)
- Patients subsequently confirmed not to have RA during the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Generative AI prediction report for RA complicationsGenerative 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. | Other | RA patient group using generative AI prediction reports |
Primary outcome
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.
1 secondary outcome
To what extent are RA complication diagnoses actually affected by GenAI predictions?
Time frame: Immediately after making the final diagnosis at discharge.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Oct 2026.
- Our previous study benchmarking LLMs for predicting a range of diseases.Link provided by the sponsor.
- A recent paper reviewing LLMs in clinical decision support for autoimmune diseases.Link provided by the sponsor.
- Individual participant dataSharing planned: No
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Rheumatoid Arthritis (RA and Osteoporosis, run by Guang'anmen Hospital of China Academy of Chinese Medical Sciences at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Will physicians adopt GenAI predictions in diagnosing RA complications? (time frame: Immediately after reviewing patient AI report on the day of admission.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Oct 2026.