Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis
NCT ID: NCT07301892
Last Updated: 2025-12-24
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
RECRUITING
100 participants
OBSERVATIONAL
2025-10-01
2026-06-30
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Keywords
Explore important study keywords that can help with search, categorization, and topic discovery.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
CROSS_SECTIONAL
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
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.
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.
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
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.
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* All real-world RA inpatients admitted to our department.
* Admission occurring within the real-world data study period.
Exclusion Criteria
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Guang'anmen Hospital of China Academy of Chinese Medical Sciences
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Quan Jiang
Director of the Rheumatology Department
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Guang'anmen Hospital of China Academy of Chinese Medical Sciences
Beijing, Beijing Municipality, China
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Quan Jiang Guang'anmen Hospital, China Academy of Chinese Medical Science
Role: CONTACT
Phone: 010-88001942
Email: [email protected]
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
Quan Jiang, MD
Role: primary
Related Links
Access external resources that provide additional context or updates about the study.
Our previous study benchmarking LLMs for predicting a range of diseases.
A recent paper reviewing LLMs in clinical decision support for autoimmune diseases.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
2025-201-KY
Identifier Type: -
Identifier Source: org_study_id