Clinical Research Directory

Browse clinical research sites, groups, and studies.

Sites Groups Studies

Back to Studies

NOT YET RECRUITING

NCT07640906

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

Sponsor: Shanghai Zhongshan Hospital

View on ClinicalTrials.gov

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.

Official title: A Multicenter Comparative Study Evaluating the Impact of an AI-Assisted Chest CT Reporting System on Real-world Radiologist Performance: The DOUBLE-ACE Study

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

OBSERVATIONAL

Enrollment

Start Date

2026-06

Completion Date

2026-12

Last Updated

2026-06-11

Healthy Volunteers

Not specified

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.

The study participants include both the radiologists whose performance is evaluated and the chest CT scans they interpret. Eligibility criteria are defined for both. 1\. Inclusion Criteria 1.1 For Radiologists 1. Board-certified radiologists specializing in or routinely performing thoracic imaging. 2. Employed at one of the participating study centers for the entire duration of both the without-AI and with-AI study periods. 3. Interpreted a minimum of eligible chest CT scans (e.g., \> 50 scans) during both the without-AI and with-AI data collection periods. 1.2 For Chest CT Scans 1. Non-contrast chest CT examinations performed for any clinical indication. 2. Scans completed and finalized during the defined with-AI or without-AI study periods. 3. Patient age 18 years or older at the time of the scan. 2\. Exclusion Criteria 2.1 For Radiologists: 1. Radiologists who joined, left, or were on extended leave (e.g., \>4 weeks) from the participating center between the with-AI and without-AI study periods. 2. Radiologists who interpreted fewer than the minimum required number of eligible scans in either study period. 3. Radiologists who voluntarily decline to have their de-identified performance data included in the study analysis. 4. Radiologists who decline to provide demographic or occupational information (e.g., years of professional experience or sex)-variables that may serve as potential confounders-will be excluded from adjusted and stratified analyses that require such covariates. 2.2 For Chest CT Scans 1. CT scans of pediatric patients (age \< 18 years). 2. Contrast-enhanced chest CT studies. 3. Studies performed for specific procedural guidance (e.g., biopsy, ablation). 4. Studies deemed technically inadequate for primary interpretation by radiologist (e.g., severe motion artifact, incomplete study). 5. Studies for which the AI system fails to generate a valid preliminary report draft. This includes possible system failures, algorithm errors, or cases where the generated draft is deemed technically unusable (e.g., empty, garbled, or based on critically flawed image analysis). 6. The lack of relevant information (diagnosis, clinical scenario, etc.). Chest CT data will be excluded from corresponding analyses if the required information, which is necessary for confounding control, subgroup analyses, or other pre-specified analyses, is unavailable. Such scenarios include data that cannot be retrospectively retrieved, incompletely recorded, or restricted due to ethical or institutional requirements.

Locations (2)

Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai

Shanghai, China

United Imaging Intelligence, Shanghai, Shanghai