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AI-Assisted Chest-CT Reporting for Enhanced Speed and Quality (The DOUBLE-ACE Study)

NCT07640906 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 75

Last updated 2026-06-11

No results posted yet for this study

Summary

The goal of this observational study is to learn if an AI assistant tool can help doctors who read chest CT scans (called radiologists) write their reports faster and just as well or better. Chest CT scans are common pictures taken of the inside of the chest to help with diagnosis. The main questions the study aims to answer are: (1) Does using the AI tool save radiologists time when writing their reports? (2) Are the final reports written with the AI tool's help as good as or better than reports written without it? To answer these questions, researchers will compare two time periods at several hospitals. They will look at how long it took to write reports and how good the reports were, both from a time before the AI tool was available and from a time after it was in regular use. In this study, radiologists will use the AI tool as part of their normal daily work. The tool is built into the computer system they already use to look at scans. Researchers will then measure the time and quality of the reports produced during their regular shifts.

Conditions

  • Thoracic Diseases
  • Chest CT Scan
  • Artificial Intelligence (AI) in Diagnosis

Interventions

DEVICE

An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretation

The intervention under evaluation is an AI-assisted diagnostic reporting system, integrated directly into the radiologists' workflow. The system analyzes the CT images in real time using an AI model and automatically generates a structured, preliminary radiology report draft. The interpreting radiologist reviews this AI-generated draft, which is presented within their familiar reporting interface. The radiologist then actively edits, confirms, supplements, or overrides the draft content as necessary before finalizing and signing the report. This intervention is distinguished from other AI tools by its focus on end-to-end reporting efficiency via integrated draft generation within the radiologist's classic workflow. It moves beyond simple abnormality detection or highlighting by generating a complete, structured narrative report draft, aiming to reduce dictation/typing time and minimize oversight of findings.

Sponsors & Collaborators

  • Shanghai Zhongshan Hospital

    lead OTHER

Principal Investigators

  • Mengsu Zeng, MD, PhD · Department of Radiology, Zhongshan Hospital, Fudan University

  • Dinggang Shen, PhD · United Imaging Intelligence, Shanghai

  • Jianying Gu, MD, PhD · Department of Radiology, Zhongshan Hospital, Fudan University

  • Dijia Wu, PhD · United Imaging Intelligence, Shanghai

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-30
Primary Completion
2026-08-31
Completion
2026-12-31

Countries

  • China

Study Locations

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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 NCT07640906 on ClinicalTrials.gov