Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Generative AI prediction report for RA complications.
- Who it may be relevant to
- Registry conditions: Rheumatoid Arthritis (RA, Osteoporosis, Osteoarthritis, Interstitial Lung Disease. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Impact of Generative Artificial Intelligence on Diagnosing Rheumatoid Arthritis Complications
Overview
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.
Interventions
- 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 re
Primary outcome measures
- Will physicians adopt GenAI predictions in diagnosing RA complications? [Time frame: Immediately after reviewing patient AI report on the day of admission.]
Secondary outcome measures (1)
- To what extent are RA complication diagnoses actually affected by GenAI predictions? [Time frame: Immediately after making the final diagnosis at discharge.]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Guang'anmen Hospital of China Academy of Chinese Medical Sciences — Beijing
Identifiers
NCT: NCT07301892 · 2025-201-KY