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
Qualifiers
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 design
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
Treatment groups
75 Participants
are divided into 1 treatment groupSponsors and collaborators
Source ClinicalTrials.gov