NCT07497243

X-ray Assisted Diagnostic System

Construction and Clinical Application of an X-ray AI-Aided Diagnosis System: A Randomized Controlled Trial

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 16,000 (estimated)
• Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

X-ray examination is one of the most commonly used imaging modalities, especially chest X-ray, which is routinely performed for hospitalized patients. However, due to the low density resolution of X-ray images, radiologists' ability to diagnose diseases-particularly small lesions-is often affected. Studies have shown that the diagnostic accuracy of radiologists using chest X-rays is only around 70%, which does not meet clinical demands. Based on this, we developed an artificial intelligence model to assist radiologists in interpreting X-ray images and generating reports, with the aim of improving diagnostic accuracy and reducing interpretation time.

Conditions

• Chest X-ray for Clinical Evaluation

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	AI-assisted radiologist diagnostic group	Based on the previously developed X-ray image diagnosis and report generation model, radiologists are assisted in interpreting X-ray images and generating reports.
DIAGNOSTIC_TEST	Radiologist diagnostic group	After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis.

Timeline

• Start date: 2026-05-01
• Primary completion: 2026-10-31
• Completion: 2026-11-30
• First posted: 2026-03-27
• Last updated: 2026-03-27

Locations

4 sites across 1 country: China