• Clinical Application of Automated Interpretation System for Chest X-Ray Images Based on Multimodal Large Models

  NCT07117266

  CompletedInterventionalNot ApplicableUpdated Sep 12, 2025
• xrAI - Improving Quality and Efficiency in Chest Radiograph Interpretation by Radiologists

  NCT04221100

  WithdrawnInterventionalNot ApplicableUpdated Feb 23, 2021
• xrAI - Improving Quality and Efficiency in Chest Radiograph Interpretation

  NCT04153045

  CompletedInterventionalNot ApplicableUpdated Sep 22, 2020
• Trial of Artificial Intelligence for Chest Radiography

  NCT06456203

  Unknown statusInterventionalNot ApplicableUpdated Jun 13, 2024
• Evaluating a Text-Prompt AI Assistant for Chest CT Scans (AI-REPORT Study)

  NCT07634861

  RecruitingInterventionalNot ApplicableUpdated Jun 9, 2026
• Artificial Intelligence-assisted Diagnosis

  NCT06783933

  Active, not recruitingObservationalUpdated Jul 14, 2025

ClinicalTrials.gov ID

NCT07497243

Lead Sponsor

Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyOTHER

Overall Status

Not yet recruiting

Study Type

Observational

Enrollment

16,000Estimated

Last Update Posted

Mar 27, 2026Actual

Start Date

May 1, 2026Estimated

Primary Completion Date

Oct 31, 2026Estimated

Completion Date

Nov 30, 2026Estimated

Official Title

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

Brief 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.

• Chest X-ray for Clinical Evaluation

• AI-assisted radiologist diagnostic group
• Radiologist diagnostic group

Ages Eligible for Study

Child (0-17)Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

Yes

Eligibility Criteria

Inclusion Criteria:

• Clinically suspected thoracic diseases (such as pneumonia, tuberculosis, or lung cancer) requiring X-ray diagnosis；
• Patients providing written informed consent for research data use；
• Complete clinical records (including chief complaints, medical history, and laboratory test results)

Exclusion Criteria:

• Substandard X-ray image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures)
• Pregnant or lactating women

Observational Model

Case-Control

Time Perspective

Prospective

Lead Sponsor

Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyOTHER

chan0812@126.com15971480677

zhao_huangxuan@sina.com18971676985