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

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

Interventions

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.

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.

Sponsors & Collaborators

• Shanghai Geriatric Medical Center

  collaborator OTHER

• Yangzhou No.1 People's Hospital

  collaborator OTHER

• The Affiliated Hospital of Xuzhou Medical University

  collaborator OTHER

• Affiliated Hospital of Jiangsu University

  collaborator OTHER

• Dushu Lake Hospital Affiliated to Soochow University

  collaborator OTHER

• China-Japan Union Hospital, Jilin University

  collaborator OTHER

• Xiangya Hospital of Central South University

  collaborator OTHER

• Lanzhou University Second Hospital

  collaborator OTHER

• First Affiliated Hospital of Xinjiang Medical University

  collaborator OTHER

• Peking University Cancer Hospital & Institute

  collaborator OTHER

• Zhongshan Hospital (Xiamen), Fudan University

  collaborator OTHER

• First People's Hospital of Kunming

  collaborator OTHER

• Shanghai Minhang Central Hospital

  collaborator OTHER

• Shanghai United Imaging Intelligence Ltd.

  collaborator UNKNOWN

• Shanghai Zhongshan Hospital

  lead OTHER

Principal Investigators

• Mengsu Zeng, MD, PhD · Department of Radiology, Zhongshan Hospital, Fudan University

• Dinggang Shen, PhD · United Imaging Intelligence, Shanghai

• Jianying Gu, MD, PhD · Department of Radiology, Zhongshan Hospital, Fudan University

• Dijia Wu, PhD · United Imaging Intelligence, Shanghai

Study Design

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Model

CROSSOVER

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-06-20

Primary Completion

2026-12-31

Completion

2027-02-15

Countries

• China

Study Locations