Thoracic DiseasesAI-REPORTNCT07634861
Evaluating a Text-Prompt AI Assistant for Chest CT Scans (AI-REPORT Study)
A study testing AI-generated report for chest CT and Standard reporting procedure (no AI assistance) in people with Thoracic Diseases, run by Shanghai Zhongshan Hospital at 2 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Thoracic Diseases
- What is being tested?
- AI-generated report for chest CT and Standard reporting procedure (no AI assistance)
- Type of treatment
- Medical device, Surgery or procedure
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 2 sitesChina
- Study dates
- 20 Jun 2026to 15 Feb 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Weiqiu Jin, BEng, BA, MD contact
China2 sites, 2 recruiting
- Recruiting
Department of Radiology, Zhongshan Hospital, Fudan University
Shanghai
Xiaodan Ye, MD, PhD, +86-13761459998, yuanyxd@163.com
Weiqiu Jin, BEng, BA, MD, jinwqzsh@fudan.edu.cn
- Recruiting
United Imaging Intelligence, Shanghai
Shanghai
Dijia Wu, PhD, 86-21-67076888, dijia.wu@uii-ai.com
Jiayu Wang, MS, jiayu.wang@uii-ai.com
About this study
Official title: 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.
Read the 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.
- 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.
- 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.
- 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.
- 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).
- Conditions
- Thoracic Diseases
- Being tested
- AI-generated report for chest CT, Standard reporting procedure (no AI assistance)
- Medical subject headings
- Thoracic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-generated report for chest CTDevice
A clinical decision support software generates a preliminary report draft for chest CT examinations. Board-certified radiologists then finalize the AI draft.
Standard reporting procedure (no AI assistance)Procedure or surgery
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Procedure: Standard reporting procedure (no AI assistance) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2026
- Study start20 Jun 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion15 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Thoracic DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Zhongshan Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai Geriatric Medical Center, Yangzhou No.1 People's Hospital, The Affiliated Hospital of Xuzhou Medical University, Affiliated Hospital of Jiangsu University, Dushu Lake Hospital Affiliated to Soochow University, China-Japan Union Hospital, Jilin University, Xiangya Hospital of Central South University, Lanzhou University Second Hospital, First Affiliated Hospital of Xinjiang Medical University, Peking University Cancer Hospital & Institute, Zhongshan Hospital (Xiamen), Fudan University, First People's Hospital of Kunming, Shanghai Minhang Central Hospital, Shanghai United Imaging Intelligence Ltd.
- Responsible party
- Sponsor
- Investigators
- Mengsu Zeng, MD, PhD, Study chair, Department of Radiology, Zhongshan Hospital, Fudan UniversityDinggang Shen, PhD, Study director, United Imaging Intelligence, ShanghaiJianying Gu, MD, PhD, Study director, Department of Radiology, Zhongshan Hospital, Fudan UniversityDijia Wu, PhD, Study director, United Imaging Intelligence, Shanghai
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07634861
- Sponsor's protocol ID
- B2025-151
- Acronym
- AI-REPORT
- Status last verified
- Jun 2026
- First submitted
- 27 May 2026
- Last updated
- 9 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Evaluation Study of a Text-Based Chest CT-Assisted Diagnostic System: A Two-stage, Multicenter, Multireader Multicase (MRMC), Self-Crossover Controlled Trial
- ClinicalTrials.gov ID
- NCT07634861
- Acronym / sponsor ID
- AI-REPORT / B2025-151
- Conditions
- Thoracic Diseases
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Shanghai Zhongshan HospitalUniversity, hospital or other
- Collaborators
- Shanghai Geriatric Medical Center, Yangzhou No.1 People's Hospital, The Affiliated Hospital of Xuzhou Medical University, Affiliated Hospital of Jiangsu University, Dushu Lake Hospital Affiliated to Soochow University, China-Japan Union Hospital, Jilin University, Xiangya Hospital of Central South University, Lanzhou University Second Hospital, First Affiliated Hospital of Xinjiang Medical University, Peking University Cancer Hospital & Institute, Zhongshan Hospital (Xiamen), Fudan University, First People's Hospital of Kunming, Shanghai Minhang Central Hospital, Shanghai United Imaging Intelligence Ltd.
- Study start
- 20 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 15 Feb 2027 (estimated)
- First posted
- 9 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
- 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.
- 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.
- 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.
- 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 design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Reporting Arm | Experimental | Device: AI-generated report for chest CTThis 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. |
| Standard Reporting | Active comparator | Procedure: Standard reporting procedure (no AI assistance)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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-generated report for chest CTA clinical decision support software generates a preliminary report draft for chest CT examinations. Board-certified radiologists then finalize the AI draft. | Device | AI-Assisted Reporting Arm |
| 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. | Procedure or surgery | Standard Reporting |
Primary outcome
Subjective report quality evaluation based on diagnostic requirements and clinical relevance
Time frame: 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.
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.
Significance of radiologist modifications to AI-generated reports
Time frame: 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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jun 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Weiqiu Jin, BEng, BA, MD contact
Mengsu Zeng, MD, PhD study chair
Department of Radiology, Zhongshan Hospital, Fudan University
Dinggang Shen, PhD study director
United Imaging Intelligence, Shanghai
Jianying Gu, MD, PhD study director
Department of Radiology, Zhongshan Hospital, Fudan University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-generated report for chest CT and Standard reporting procedure (no AI assistance) in people with Thoracic Diseases, run by Shanghai Zhongshan Hospital at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 9 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xiaodan Ye, MD, PhD (+86-13761459998, yuanyxd@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Subjective report quality evaluation based on diagnostic requirements and clinical relevance (time frame: 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.). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jun 2026.