Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis

NCT07301892 | Recruiting DMC

• 1 other identifier: 2025-201-KY
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief 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). 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

Osteoporosis; Osteoarthritis; Interstitial Lung Disease; Thyroid Diseases; Cardiovascular Diseases; Pulmonary Complications; Sjogren's Syndrome; Liver Disorders; Renal Lesions; Vasculitis; Amyloidosis; Peripheral Neuropathy; Thrombosis; RA Complications; Rheumatoid Arthritis (RA

Keywords

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

Outcome Measures

Primary Outcomes (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.

  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?

  Immediately after making the final diagnosis at discharge.

Study Arms (1)

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

Interventions

Generative AI prediction report for RA complications OTHER

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.

RA patient group using generative AI prediction reports

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Guang'anmen Hospital of China Academy of Chinese Medical Sciences: lead

Study Sites (1)

Guang'anmen Hospital of China Academy of Chinese Medical Sciences

Beijing, Beijing Municipality, 100053, China

RECRUITING

Related Links

• Our previous study benchmarking LLMs for predicting a range of diseases.
• A recent paper reviewing LLMs in clinical decision support for autoimmune diseases.

MeSH Terms

Conditions

Arthritis, Rheumatoid; Osteoporosis; Osteoarthritis; Lung Diseases, Interstitial; Thyroid Diseases; Cardiovascular Diseases; Sjogren's Syndrome; Digestive System Diseases; Vasculitis; Amyloidosis; Peripheral Nervous System Diseases; Thrombosis

Condition Hierarchy (Ancestors)

Arthritis; Joint Diseases; Musculoskeletal Diseases; Rheumatic Diseases; Connective Tissue Diseases; Skin and Connective Tissue Diseases; Autoimmune Diseases; Immune System Diseases; Bone Diseases, Metabolic; Bone Diseases; Metabolic Diseases; Nutritional and Metabolic Diseases; Lung Diseases; Respiratory Tract Diseases; Endocrine System Diseases; Xerostomia; Salivary Gland Diseases; Mouth Diseases; Stomatognathic Diseases; Dry Eye Syndromes; Lacrimal Apparatus Diseases; Eye Diseases; Vascular Diseases; Proteostasis Deficiencies; Neuromuscular Diseases; Nervous System Diseases; Embolism and Thrombosis

Central Study Contacts

Quan Jiang Guang'anmen Hospital, China Academy of Chinese Medical Science

CONTACT

010-88001942; doctorjq@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director of the Rheumatology Department

Study Record Dates

• First Submitted: September 28, 2025
• First Posted: December 24, 2025
• Study Start: October 1, 2025
• Primary Completion: February 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: December 24, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)