Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis

ConditionsRheumatoid Arthritis (RA 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

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

AgeNot listed

SponsorGuang'anmen Hospital of China Academy of Chinese Medical Sciences

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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.

Disqualifiers

Patients subsequently confirmed not to have RA during the study.

Trial 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 design

Design model

Cohort

Time perspective

Cross-sectional

Treatments tested in this trial

Generative AI prediction report for RA complications
Other intervention
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.

Treatment groups

100 Participants

are divided into 1 treatment group

Group A: RA patient group using generative AI prediction reports1 intervention

Trial outcomes

Primary outcomes

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.

Time frame

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

Secondary outcomes

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.

Time frame

Immediately after making the final diagnosis at discharge.

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Guang'anmen Hospital of China Academy of Chinese Medical Sciences

Lead sponsor

Source ClinicalTrials.gov

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