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

NCT07634861 | Recruiting DMC

• 1 other identifier: B2025-151
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 2

Brief 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.

Conditions

Thoracic Diseases

Outcome Measures

Primary Outcomes (2)

• 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.

Study Arms (2)

AI-Assisted Reporting Arm

EXPERIMENTAL

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

Standard Reporting

ACTIVE COMPARATOR

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)

Interventions

Standard reporting procedure (no AI assistance) PROCEDURE

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.

Standard Reporting

AI-generated report for chest CT DEVICE

A clinical decision support software generates a preliminary report draft for chest CT examinations. Board-certified radiologists then finalize the AI draft.

AI-Assisted Reporting Arm

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Shanghai Zhongshan Hospital: lead
• Shanghai Geriatric Medical Center: collaborator
• Yangzhou No.1 People's Hospital: collaborator
• The Affiliated Hospital of Xuzhou Medical University: collaborator
• Affiliated Hospital of Jiangsu University: collaborator
• Dushu Lake Hospital Affiliated to Soochow University: collaborator
• China-Japan Union Hospital, Jilin University: collaborator
• Xiangya Hospital of Central South University: collaborator
• Lanzhou University Second Hospital: collaborator
• First Affiliated Hospital of Xinjiang Medical University: collaborator
• Peking University Cancer Hospital & Institute: collaborator
• Zhongshan Hospital (Xiamen), Fudan University: collaborator
• First People's Hospital of Kunming: collaborator
• Shanghai Minhang Central Hospital: collaborator
• Shanghai United Imaging Intelligence Ltd.: collaborator

Study Sites (2)

Department of Radiology, Zhongshan Hospital, Fudan University

Shanghai, China

RECRUITING

United Imaging Intelligence, Shanghai

Shanghai, China

RECRUITING

MeSH Terms

Conditions

Thoracic Diseases

Condition Hierarchy (Ancestors)

Respiratory Tract Diseases

Study Officials

• Mengsu Zeng, MD, PhD

  Department of Radiology, Zhongshan Hospital, Fudan University

  STUDY CHAIR

• 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 DIRECTOR

Central Study Contacts

Xiaodan Ye, MD, PhD

CONTACT

+86-13761459998; yuanyxd@163.com

Weiqiu Jin, BEng, BA, MD

CONTACT

jinwqzsh@fudan.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 27, 2026
• First Posted: June 9, 2026
• Study Start (Estimated): June 20, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): February 15, 2027
• Last Updated: June 9, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)