MedTrace Logo

Generative AI Impact on Rheumatoid Arthritis Complications Diagnosis

NCT07301892 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2025-12-24

No results posted yet for this study

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

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 reports will be validated using EHR records from retrospective RA patients.

Sponsors & Collaborators

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

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-10-01
Primary Completion
2026-02-28
Completion
2026-06-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07301892 on ClinicalTrials.gov