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
調査の概要
状態
条件
詳細な説明
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).
- 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.
- 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
- 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).
- 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
- Radiologist Readers: Board-certified radiologists with ≥ 3 years of independent thoracic imaging practice.
- 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).
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前：Xiaodan Ye, MD, PhD
- 電話番号：+86-13761459998
- メール：yuanyxd@163.com
研究連絡先のバックアップ
- 名前：Weiqiu Jin, BEng, BA, MD
- メール：jinwqzsh@fudan.edu.cn
研究場所
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Shanghai、中国
- 募集
- Department of Radiology, Zhongshan Hospital, Fudan University
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主任研究者：
- Mengsu Zeng, MD, PhD
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コンタクト：
- Xiaodan Ye, MD, PhD
- 電話番号：+86-13761459998
- メール：yuanyxd@163.com
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コンタクト：
- Weiqiu Jin, BEng, BA, MD
- メール：jinwqzsh@fudan.edu.cn
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Shanghai、中国
- 募集
- United Imaging Intelligence, Shanghai
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コンタクト：
- Dijia Wu, PhD
- 電話番号：86-21-67076888
- メール：dijia.wu@uii-ai.com
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コンタクト：
- Jiayu Wang, MS
- メール：jiayu.wang@uii-ai.com
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主任研究者：
- Dinggang Shen, PhD
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
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
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：診断
- 割り当て：ランダム化
- 介入モデル：クロスオーバー割り当て
- マスキング：なし（オープンラベル）
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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実験的：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.
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A clinical decision support software generates a preliminary report draft for chest CT examinations.
Board-certified radiologists then finalize the AI draft.
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アクティブコンパレータ：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.
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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.
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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Subjective report quality evaluation based on diagnostic requirements and clinical relevance
時間枠：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.
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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.
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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.
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Significance of radiologist modifications to AI-generated reports
時間枠：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.
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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.
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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.
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協力者と研究者
協力者
捜査官
- スタディチェア：Mengsu Zeng, MD, PhD、Department of Radiology, Zhongshan Hospital, Fudan University
- スタディディレクター：Dinggang Shen, PhD、United Imaging Intelligence, Shanghai
- スタディディレクター：Jianying Gu, MD, PhD、Department of Radiology, Zhongshan Hospital, Fudan University
- スタディディレクター：Dijia Wu, PhD、United Imaging Intelligence, Shanghai
研究記録日
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研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
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最終確認日
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