Evaluating a Text-Prompt AI Assistant for Chest CT Scans (AI-REPORT Study)
An Evaluation Study of a Text-Based Chest CT-Assisted Diagnostic System: A Two-stage, Multicenter, Multireader Multicase (MRMC), Self-Crossover Controlled Trial
Shanghai Zhongshan Hospital
100 participants
Jun 20, 2026
INTERVENTIONAL
Conditions
Summary
This study aims to find out if an artificial intelligence (AI) system can help experienced radiologists write chest CT scan reports more quickly without lowering the quality of the report. Chest CT scans are common, and writing reports for them is a major part of a radiologist's job. In this trial, board-certified radiologists will interpret complex chest CT cases. For some cases, they will start with a complete draft report generated by the AI system, which they can review and edit as needed. For other cases, they will write the report from scratch without any AI help, following their usual routine. The main things we are measuring are: 1) how much time the AI draft saves, and 2) whether the final reports created with AI help are as good as or better than those written without it, as judged by other senior doctors who do not know which report came from which method. The hope is that this AI tool can make radiologists' work more efficient while maintaining high standards for patient care.
Eligibility
Inclusion Criteria4
- Active board certification and ongoing routine clinical practice as an attending radiologist
- Independent institutional authority for chest CT image interpretation and final official diagnostic report issuance
- A minimum of three years of post-certification clinical experience in specialized thoracic imaging
- Legal and cognitive competence for study participation, with voluntary provision of written informed consent after full understanding of study purpose, procedures, risks and benefits
Exclusion Criteria3
- Direct participation in the development, training or validation of the trial's evaluated AI system
- Ongoing participation in concurrent studies with potential risks of interpretation bias, cognitive fatigue or study procedure interference (investigator-assessed)
- Any actual or perceived conflict of interest related to the evaluated AI system or its developers that may compromise objectivity in image interpretation and diagnostic reporting
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Interventions
A clinical decision support software generates a preliminary report draft for chest CT examinations. Board-certified radiologists then finalize the AI draft.
Standard chest CT reporting procedure without AI assistance. Board-certified radiologists independently interpret chest CT examinations and generate final reports following standard clinical workflow without preliminary AI-generated drafts.
Locations(2)
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NCT07634861