Evaluating a Text-Prompt AI Assistant for Chest CT Scans (AI-REPORT Study) (AI-REPORT)

An Evaluation Study of a Text-Based Chest CT-Assisted Diagnostic System: A Two-stage, Multicenter, Multireader Multicase (MRMC), Self-Crossover Controlled Trial

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.

Study Overview

• Status: Recruiting
• Conditions: Thoracic Diseases
• Intervention / Treatment: Device: AI-generated report for chest CT; Procedure: Standard reporting procedure (no AI assistance)

Detailed Description

This study investigates whether an artificial intelligence (AI) system that drafts preliminary radiology reports can help experienced chest CT radiologists work faster while maintaining or improving report quality. The trial is conducted in two sequential phases. The first phase uses a set of complex, real-world historical cases. Radiologists interpret these cases both with and without the help of the AI-generated draft (AI-report) in a controlled, crossover study design. The second phase is a prospective, real-world deployment where the same AI-report system is integrated into the clinical workflow of participating radiologists as they interpret new, incoming chest CT scans in real time. We measure the time it takes to complete reports and, through blinded evaluations by other senior doctors, assess the quality of the final reports created with and without AI assistance. The goal is to determine if this AI tool can make radiologists' work more efficient and support high-quality patient care in actual practice.

1. Detailed Description

1.1 Study Design

This is a two-phase, multicenter, multireader, multicase (MRMC) study designed to evaluate the real-world clinical utility of an AI report generation system (AI-report).

1. Stage 1 (controlled crossover evaluation): This stage employs a retrospective, randomized, two-period crossover design. A curated set of complex historical chest CT cases, previously discussed in multidisciplinary team (MDT) meetings, is used. Each participating radiologist acts as their own control, interpreting the same cases both with and without the AI draft under controlled conditions.
2. Stage 2 (prospective real-world deployment): This stage is a prospective, observational study. The validated AI-report system is deployed into the live clinical workflow of the participating radiologists. They use the system in real-time as they interpret new, consecutive chest CT scans from their clinical duties, allowing for evaluation in an authentic clinical environment.

1.2 Objectives

1. Primary objectives: To evaluate the impact of the AI-report system on 1) radiologist efficiency (interpretation time) and 2) the clinical quality of finalized reports, assessed in both a controlled retrospective setting (Phase 1) and a prospective real-world setting (Phase 2).
2. Secondary objectives: To assess the nature and clinical significance of edits made to AI drafts, and to evaluate system usability and integration into the routine reporting workflow.

1.3 Study Population

1. Radiologist Readers: Board-certified radiologists with ≥ 3 years of independent thoracic imaging practice.
2. Blinded Evaluators: Eleven senior clinicians from the original MDT panels that contributed the Phase 1 cases, responsible for blinded quality assessment.

1.4 Intervention

The intervention is the provision of a fully AI-generated draft radiology report (AI-report). In Phase 1, this is provided within a controlled reading platform for historical cases. In Phase 2, the system is integrated into the clinical Picture Archiving and Communication System (PACS)/Radiology Information System (RIS) to generate drafts for prospective, real-time cases.

2. Study Procedures

Phase 1 (Retrospective Crossover): The 400 historical MDT cases are used. The study involves two reading rounds with a washout period. In each round, radiologists interpret a set of cases, with the AI condition (draft provided or not) randomized and crossed over between rounds. Interpretation time is recorded, and all finalized reports are collected for blinded pairwise comparison by the evaluator panel.

Phase 2 (Prospective Deployment): Following Phase 1, the AI-report system is activated in the clinical environment for participating radiologists. During a defined prospective observation period, the system generates drafts for eligible new chest CT scans. Radiologists use these drafts in their daily work. Reporting time and the AI drafts alongside the finalized human-edited reports are collected for analysis. Report quality in this phase is assessed longitudinally and through sampling.

3. Outcome Measures

3.1 Primary Outcomes:

Efficiency: Change in median interpretation time per case with vs. without AI-report assistance (Phase 1) and the distribution of reporting times during real-world use (Phase 2).

Quality: Superiority score from blinded paired comparisons of AI-assisted vs. unassisted reports (Phase 1). Qualitative and quantitative assessment of report adequacy in the prospective cohort (Phase 2).

3.2 Secondary Outcomes:

Clinical significance of radiologist modifications to AI drafts (5-point scale).

System usability and workflow integration scores from post-study surveys.

4. Statistical Analysis

Analysis will account for the MRMC design in Phase 1 using hierarchical models. Phase 2 data will be analyzed using descriptive statistics and statistical process control methods where appropriate. The two phases will be analyzed separately to provide insights into efficacy (Phase 1) and effectiveness (Phase 2).

• Study Type: Interventional
• Enrollment (Estimated): 100
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Xiaodan Ye, MD, PhD; Phone Number: +86-13761459998; Email: yuanyxd@163.com
• Study Contact Backup: Name: Weiqiu Jin, BEng, BA, MD; Email: jinwqzsh@fudan.edu.cn

Study Locations

• China
  • Shanghai, China
    • Recruiting
    • Department of Radiology, Zhongshan Hospital, Fudan University
    • Principal Investigator: Mengsu Zeng, MD, PhD
    • Contact: Xiaodan Ye, MD, PhD; Phone Number: +86-13761459998; Email: yuanyxd@163.com
    • Contact: Weiqiu Jin, BEng, BA, MD; Email: jinwqzsh@fudan.edu.cn
  • Shanghai, China
    • Recruiting
    • United Imaging Intelligence, Shanghai
    • Contact: Dijia Wu, PhD; Phone Number: 86-21-67076888; Email: dijia.wu@uii-ai.com
    • Contact: Jiayu Wang, MS; Email: jiayu.wang@uii-ai.com
    • Principal Investigator: Dinggang Shen, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• 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 Criteria:

• 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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: AI-Assisted Reporting Arm; This arm involves board-certified radiologists interpreting chest CT cases using the AI system, which generates a preliminary report draft. In Phase 1 (retrospective crossover), each radiologist interprets the same set of historical cases twice: once with the AI-generated draft and once without, with order randomized and a washout period. In Phase 2 (prospective real-world deployment), radiologists use AI drafts for consecutive new chest CT scans in routine practice. The intervention is the provision of the AI-generated report draft; no other changes to standard workflow are introduced.	Device: AI-generated report for chest CT; A clinical decision support software generates a preliminary report draft for chest CT examinations. Board-certified radiologists then finalize the AI draft.
Active Comparator: Standard Reporting; This arm involves board-certified radiologists interpreting chest CT cases without AI assistance, following standard workflow procedures. In Phase 1 (retrospective crossover), radiologists interpret the same set of historical cases without the AI-generated draft (order randomized with a washout period). In Phase 2 (prospective real-world deployment), this arm represents routine clinical practice where no AI drafts are provided for new chest CT scans. The control condition is standard reporting without AI assistance.	Procedure: Standard reporting procedure (no AI assistance); 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Subjective report quality evaluation based on diagnostic requirements and clinical relevance	Quality is blindly assessed by independent clinicians using pairwise comparisons among three report types: AI-generated raw reports, human-only reports, and human-AI collaborative reports. Superior reports score 1 point, ties score 0.5.	CT reports will be distributed for external clinician scoring once all required data are available (typically ≤ 2 weeks post Primary Completion Date); the final aggregated analysis will be completed within 4 weeks post Primary Completion Date.
Significance of radiologist modifications to AI-generated reports	Using a 5-point ordinal scale, independent external clinicians rate the clinical significance of edits made to AI reports. Level 1 denotes minimal changes; Level 5 indicates critical corrections preventing inappropriate/delayed management. Intermediate levels (2-4) represent minor adjustments, beneficial optimizations, and significant refinements impacting diagnostic clarity or treatment selection.	CT reports will be distributed for external clinician scoring once all required data are available (typically ≤ 2 weeks post Primary Completion Date); the final aggregated analysis will be completed within 4 weeks post Primary Completion Date.

Collaborators and Investigators

• Sponsor: Shanghai Zhongshan Hospital
• Collaborators: Xiangya Hospital of Central South University; Peking University Cancer Hospital & Institute; First People's Hospital of Kunming; Dushu Lake Hospital Affiliated to Soochow University; First Affiliated Hospital of Xinjiang Medical University; Affiliated Hospital of Jiangsu University; Yangzhou No.1 People's Hospital; The Affiliated Hospital of Xuzhou Medical University; China-Japan Union Hospital, Jilin University; Shanghai Minhang Central Hospital; Lanzhou University Second Hospital; Zhongshan Hospital (Xiamen), Fudan University; Shanghai Geriatric Medical Center; Shanghai United Imaging Intelligence Ltd.
• Investigators: Study Chair: Mengsu Zeng, MD, PhD, Department of Radiology, Zhongshan Hospital, Fudan University; Study Director: Dinggang Shen, PhD, United Imaging Intelligence, Shanghai; Study Director: Jianying Gu, MD, PhD, Department of Radiology, Zhongshan Hospital, Fudan University; Study Director: Dijia Wu, PhD, United Imaging Intelligence, Shanghai

Study record dates

Study Major Dates

• Study Start (Estimated): June 20, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): February 15, 2027

Study Registration Dates

• First Submitted: May 27, 2026
• First Submitted That Met QC Criteria: June 7, 2026
• First Posted (Actual): June 9, 2026

Study Record Updates

• Last Update Posted (Actual): June 9, 2026
• Last Update Submitted That Met QC Criteria: June 7, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Thoracic Diseases
• Other Study ID Numbers: B2025-151

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No