AI-Assisted Chest-CT Reporting for Enhanced Speed and Quality (The DOUBLE-ACE Study)
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorShanghai Zhongshan Hospital
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Board-certified radiologists specializing in or routinely performing thoracic imaging.
Employed at one of the participating study centers for the entire duration of both the without-AI and with-AI study periods.
Interpreted a minimum of eligible chest CT scans (e.g., > 50 scans) during both the without-AI and with-AI data collection periods.
Non-contrast chest CT examinations performed for any clinical indication.
Disqualifiers
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.
Radiologists who interpreted fewer than the minimum required number of eligible scans in either study period.
Radiologists who voluntarily decline to have their de-identified performance data included in the study analysis.
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.
Trial population
The study population is defined as follows: 1. Radiologists: Attending radiologists who routinely interpret chest CT scans as part of their clinical duties at the participating medical centers. 2. Chest CT Scans: Clinically indicated, non-contrast chest CT examinations acquired from the patient populations served by the participating centers. The scans are stratified into two groups based on the date of acquisition: before and after the implementation of the AI reporting system.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretation
DeviceThe 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.
Treatment groups
75 Participants
are divided into 1 treatment groupGroup A: Radiologists with/without chest CT interpretation AI assistant1 intervention
Trial outcomes
Primary outcomes
1
Change in Average Image Interpretation Time
Comparison of the average time taken by participating radiologists to complete standard chest CT interpretation tasks, measured both with and without use of the automated interpretation tool. The time will be recorded from the start to the completion of each individual reading case.
Time frame
Time of interpretation will be collected once the data become fully available (generally within 2 weeks after the planned primary completion date). Final aggregated analysis will be completed within 3 months after the collection of potential confounders.2
Change in Chest CT Report Quality Score
Comparison of the subjective quality of chest CT reports written with and without automated tool support. Blinded external experts will evaluate the subjective quality of all sampled reports using a 10-point rating scale, with scores ranging from 1 (poorest quality) to 10 (highest quality).
Time frame
Reports will be distributed to external experts for scoring once the data become available, with scoring results returned within 7 days. Final aggregated analysis will be completed within 3 months after the collection of potential confounders.Other outcomes
1
Radiologist Editing Intensity on AI-Generated Report Drafts
This measure quantifies the extent of modification a radiologist makes to the initial draft report generated by the AI system. Editing intensity will be algorithmically calculated for each report in the with-AI period. A common method is the normalized edit distance (e.g., Levenshtein distance) or the percentage of text modified between the AI-generated draft and the radiologist's final signed report.
Time frame
Report texts will be collected once the data become fully available (generally within 2 weeks after the planned primary completion date). Final aggregated analysis will be completed within 3 months after the collection of potential confounders.Sponsors and contacts
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Source ClinicalTrials.gov
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