Thoracic DiseasesDOUBLE-ACENCT07640906
AI-Assisted Chest-CT Reporting for Enhanced Speed and Quality (The DOUBLE-ACE Study)
An observational study of Thoracic Diseases and Chest CT Scan, run by Shanghai Zhongshan Hospital at 2 sites in China.
Trial summary
The goal of this observational study is to learn if an AI assistant tool can help doctors who read chest CT scans (called radiologists) write their reports faster and just as well or better. Chest CT scans are common pictures taken of the inside of the chest to help with diagnosis. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Thoracic Diseases, Chest CT Scan, Artificial Intelligence (AI) in Diagnosis
- What is being tested?
- An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretation
- Type of treatment
- Medical device
- How many people
- 75 participants
- Where
- 2 sitesChina
- Study dates
- Jun 2026to Dec 2026
- Phase
- Observational
- 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
- Adult, Older adult
Study population: The study population is defined as follows: 1. Radiologists: Attending radiologists who routinely interpret chest CT scans as part of their clinical duties at the participating medical centers. 2. Chest CT Scans: Clinically indicated, non-contrast chest CT examinations acquired from the patient populations served by the participating centers. The scans are stratified into two groups based on the date of acquisition: before and after the implementation of the AI reporting system.
The study participants include both the radiologists whose performance is evaluated and the chest CT scans they interpret. Eligibility criteria are defined for both.
- Inclusion Criteria
1.1 For Radiologists
- Board-certified radiologists specializing in or routinely performing thoracic imaging.
- Employed at one of the participating study centers for the entire duration of both the without-AI and with-AI study periods.
- Interpreted a minimum of eligible chest CT scans (e.g., > 50 scans) during both the without-AI and with-AI data collection periods.
1.2 For Chest CT Scans
- Non-contrast chest CT examinations performed for any clinical indication.
- Scans completed and finalized during the defined with-AI or without-AI study periods.
- Patient age 18 years or older at the time of the scan.
- Exclusion Criteria
2.1 For Radiologists:
- Radiologists who joined, left, or were on extended leave (e.g., >4 weeks) from the participating center between the with-AI and without-AI study periods.
- Radiologists who interpreted fewer than the minimum required number of eligible scans in either study period.
- Radiologists who voluntarily decline to have their de-identified performance data included in the study analysis.
- Radiologists who decline to provide demographic or occupational information (e.g., years of professional experience or sex)-variables that may serve as potential confounders-will be excluded from adjusted and stratified analyses that require such covariates.
2.2 For Chest CT Scans
- CT scans of pediatric patients (age < 18 years).
- Contrast-enhanced chest CT studies.
- Studies performed for specific procedural guidance (e.g., biopsy, ablation).
- Studies deemed technically inadequate for primary interpretation by radiologist (e.g., severe motion artifact, incomplete study).
- Studies for which the AI system fails to generate a valid preliminary report draft. This includes possible system failures, algorithm errors, or cases where the generated draft is deemed technically unusable (e.g., empty, garbled, or based on critically flawed image analysis).
- The lack of relevant information (diagnosis, clinical scenario, etc.). Chest CT data will be excluded from corresponding analyses if the required information, which is necessary for confounding control, subgroup analyses, or other pre-specified analyses, is unavailable. Such scenarios include data that cannot be retrospectively retrieved, incompletely recorded, or restricted due to ethical or institutional requirements.
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, MD contact
China2 sites
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai
Shanghai
Xiaodan Ye, MD, PhD, +86-13761459998, yuanyxd@163.com
Weiqiu Jin, BEng, BA, MD, jinwqzsh@fudan.edu.cn
United Imaging Intelligence, Shanghai, 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: A Multicenter Comparative Study Evaluating the Impact of an AI-Assisted Chest CT Reporting System on Real-world Radiologist Performance: The DOUBLE-ACE Study
The goal of this observational study is to learn if an AI assistant tool can help doctors who read chest CT scans (called radiologists) write their reports faster and just as well or better. Chest CT scans are common pictures taken of the inside of the chest to help with diagnosis. The main questions the study aims to answer are: (1) Does using the AI tool save radiologists time when writing their reports? (2) Are the final reports written with the AI tool's help as good as or better than reports written without it? To answer these questions, researchers will compare two time periods at several hospitals. They will look at how long it took to write reports and how good the reports were, both from a time before the AI tool was available and from a time after it was in regular use. In this study, radiologists will use the AI tool as part of their normal daily work. The tool is built into the computer system they already use to look at scans. Researchers will then measure the time and quality of the reports produced during their regular shifts.
Read the detailed description
Here we provide a summary of the study's methodological framework, including a description of the AI system under evaluation, key quality control measures, and the data analysis plan. Comprehensive details regarding the full protocol, including eligibility criteria and outcome measures, can be found in the other modules of the study protocol.
- Background on the AI System: The study evaluates a clinically deployed AI-assisted reporting system, built upon an advanced multimodal foundation model trained on a large-scale chest CT dataset. Its performance, reliability, and generalizability have been established through rigorous validation on extensive internal and external datasets. Prior reader studies have demonstrated its clinical utility by significantly reducing reporting time through automated draft generation while maintaining or improving report quality, supporting its integration into real-world workflow for this evaluation.
- Quality Control: To ensure objective assessment, report quality will be scored by a panel of at least two independent, blinded thoracic radiologists using a standardized rubric, with inter-rater reliability calculated. A study-specific data dictionary and Standard Operating Procedures (SOPs) for data handling and analysis will be implemented to ensure reproducibility and auditability.
- Data Analysis Plan: A comparative statistical analysis between pre- and post-implementation groups is planned. Appropriate statistical tests (e.g., Mann-Whitney U test, mixed-effects models) will be applied based on data distribution and variable type. A sample size calculation will be conducted to ensure the study is adequately powered to detect a clinically meaningful difference in the primary efficiency endpoint. The primary analysis will be a paired comparison of outcomes (e.g., report time, quality) between the two phases for the participants who complete both. To address potential attrition (e.g., radiologist turnover during the study year) and the influence of radiologist experience, the analysis plan includes: (1) Accounting for and reporting any participant dropout between phases. (2) Conducting pre-specified subgroup or stratified analyses based on radiologist seniority (e.g., junior vs. senior) to examine its effect on the outcomes.
- Confounding Factor Control: To minimize potential bias, the study may identify collective variables (radiologists' sex, years of relevant professional experience, etc.) that may be considered potential confounding factors according to external experts' judgements. Certain patient-related information, such as diagnosis (infection, malignancy, cardiovascular disease, etc.) and clinical scenario (e.g., inpatient, outpatient, emergency), may also be collected retrospectively, where necessary, to evaluate model performance within specific diagnostic subgroups. The study will adopt multiple possible approaches for confounding factor adjustment or analysis, which may include stratified analyses and other related statistical methods.
- Potential Adjustments in Study Protocol or Analysis Methods: As the study may involve multiple centers, in case of ethical or administrative restrictions at certain time at specific sites, AI assistance may be temporarily suspended to approximate the scenario without AI assistance at those sites. In such cases, the corresponding results may be reported as the with-AI and without-AI phases, rather than labeling them as baseline and AI-available phases. Furthermore, radiologists who decline to provide demographic or occupational information (e.g., years of professional experience or sex)-variables that may serve as potential confounders-will be excluded from adjusted and stratified analyses that require such covariates. These approaches may need to be incorporated into confounding factor or stratified analyses, and we may update related conditions accordingly when necessary.
- Being tested
- An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretation
- Keywords
- Chest CT scan, artificial intelligence
- Medical subject headings
- Thoracic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretationDevice
The intervention under evaluation is an AI-assisted diagnostic reporting system, integrated directly into the radiologists' workflow. The system analyzes the CT images in real time using an AI model and automatically generates a structured, preliminary radiology report draft. The interpreting radiologist reviews this AI-generated draft, which is presented within their familiar reporting interface. The radiologist then actively edits, confirms, supplements, or overrides the draft content as necessary before finalizing and signing the report. This intervention is distinguished from other AI tools by its focus on end-to-end reporting efficiency via integrated draft generation within the radiologist's classic workflow. It moves beyond simple abnormality detection or highlighting by generating a complete, structured narrative report draft, aiming to reduce dictation/typing time and minimize oversight of findings.
| Study group | Role | What they receive |
|---|---|---|
| Radiologists with/without chest CT interpretation AI assistant | This study employs a single-arm, within-subjects design. A cohort of radiologists will be followed through two sequential practice phases: (1) Baseline (Control) Phase: Participants interpret and report on chest CT scans using their standard clinical workflow without AI assistance. (2) AI-available phase: The same participants interpret and report on a different set of chest CT scans with the integrated AI-assisted reporting system activated in their workflow. Receives: Device: An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in Average Image Interpretation Time
Comparison of the average time taken by participating radiologists to complete standard chest CT interpretation tasks, measured both with and without use of the automated interpretation tool. The time will be recorded from the start to the completion of each individual reading case.
Measured: Time of interpretation will be collected once the data become fully available (generally within 2 weeks after the planned primary completion date). Final aggregated analysis will be completed within 3 months after the collection of potential confounders.
Change in Chest CT Report Quality Score
Comparison of the subjective quality of chest CT reports written with and without automated tool support. Blinded external experts will evaluate the subjective quality of all sampled reports using a 10-point rating scale, with scores ranging from 1 (poorest quality) to 10 (highest quality).
Measured: Reports will be distributed to external experts for scoring once the data become available, with scoring results returned within 7 days. Final aggregated analysis will be completed within 3 months after the collection of potential confounders.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jun 2026
- Study startJun 2026 (estimated)
- Primary completionAug 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Thoracic DiseasesStudies of the same condition, open and under way.
- More trials run by Shanghai Zhongshan HospitalThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 75 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Zhongshan Hospital
- Sponsor type
- University, hospital or other
- 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
- NCT07640906
- Sponsor's protocol ID
- B2025-151(2)
- Acronym
- DOUBLE-ACE
- Status last verified
- Jun 2026
- First submitted
- 27 May 2026
- Last updated
- 11 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This is an observational study analyzing aggregated, de-identified operational metrics (e.g., radiologist efficiency, report quality scores) derived from routine clinical practice. The data are not collected as part of a prospective clinical trial and are not structured for independent analysis. Findings will be disseminated through peer-reviewed publications.
Publications
Teo ZL, Thirunavukarasu AJ, Elangovan K, Cheng H, Moova P, Soetikno B, Nielsen C, Pollreisz A, Ting DSJ, Morris RJT, Shah NH, Langlotz CP, Ting DSW. Generative artificial intelligence in medicine. Nat Med. 2025 Oct;31(10):3270-3282. doi: 10.1038/s41591-025-03983-2. Epub 2025 Oct 6.
PubMed 41053447Chen SF, Alyakin A, Seas A, Yang E, Choi JJ, Lee JV, Chen AL, Warman PI, Bitolas RT, Steele RJ, Alber DA, Oermann EK. LLM-assisted systematic review of large language models in clinical medicine. Nat Med. 2026 Mar;32(3):1152-1159. doi: 10.1038/s41591-026-04229-5. Epub 2026 Mar 3.
PubMed 41776077
Study at a glance
From the registry record, last updated by the sponsor on 11 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicenter Comparative Study Evaluating the Impact of an AI-Assisted Chest CT Reporting System on Real-world Radiologist Performance: The DOUBLE-ACE Study
- ClinicalTrials.gov ID
- NCT07640906
- Acronym / sponsor ID
- DOUBLE-ACE / B2025-151(2)
- Keywords
- Chest CT scan, artificial intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 75 participants (estimated)
- Lead sponsor
- Shanghai Zhongshan HospitalUniversity, hospital or other
- Study start
- Jun 2026 (estimated)
- Primary completion
- Aug 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 11 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.
The goal of this observational study is to learn if an AI assistant tool can help doctors who read chest CT scans (called radiologists) write their reports faster and just as well or better. Chest CT scans are common pictures taken of the inside of the chest to help with diagnosis. The main questions the study aims to answer are: (1) Does using the AI tool save radiologists time when writing their reports? (2) Are the final reports written with the AI tool's help as good as or better than reports written without it? To answer these questions, researchers will compare two time periods at several hospitals. They will look at how long it took to write reports and how good the reports were, both from a time before the AI tool was available and from a time after it was in regular use. In this study, radiologists will use the AI tool as part of their normal daily work. The tool is built into the computer system they already use to look at scans. Researchers will then measure the time and quality of the reports produced during their regular shifts.
Detailed description
Here we provide a summary of the study's methodological framework, including a description of the AI system under evaluation, key quality control measures, and the data analysis plan. Comprehensive details regarding the full protocol, including eligibility criteria and outcome measures, can be found in the other modules of the study protocol.
- Background on the AI System: The study evaluates a clinically deployed AI-assisted reporting system, built upon an advanced multimodal foundation model trained on a large-scale chest CT dataset. Its performance, reliability, and generalizability have been established through rigorous validation on extensive internal and external datasets. Prior reader studies have demonstrated its clinical utility by significantly reducing reporting time through automated draft generation while maintaining or improving report quality, supporting its integration into real-world workflow for this evaluation.
- Quality Control: To ensure objective assessment, report quality will be scored by a panel of at least two independent, blinded thoracic radiologists using a standardized rubric, with inter-rater reliability calculated. A study-specific data dictionary and Standard Operating Procedures (SOPs) for data handling and analysis will be implemented to ensure reproducibility and auditability.
- Data Analysis Plan: A comparative statistical analysis between pre- and post-implementation groups is planned. Appropriate statistical tests (e.g., Mann-Whitney U test, mixed-effects models) will be applied based on data distribution and variable type. A sample size calculation will be conducted to ensure the study is adequately powered to detect a clinically meaningful difference in the primary efficiency endpoint. The primary analysis will be a paired comparison of outcomes (e.g., report time, quality) between the two phases for the participants who complete both. To address potential attrition (e.g., radiologist turnover during the study year) and the influence of radiologist experience, the analysis plan includes: (1) Accounting for and reporting any participant dropout between phases. (2) Conducting pre-specified subgroup or stratified analyses based on radiologist seniority (e.g., junior vs. senior) to examine its effect on the outcomes.
- Confounding Factor Control: To minimize potential bias, the study may identify collective variables (radiologists' sex, years of relevant professional experience, etc.) that may be considered potential confounding factors according to external experts' judgements. Certain patient-related information, such as diagnosis (infection, malignancy, cardiovascular disease, etc.) and clinical scenario (e.g., inpatient, outpatient, emergency), may also be collected retrospectively, where necessary, to evaluate model performance within specific diagnostic subgroups. The study will adopt multiple possible approaches for confounding factor adjustment or analysis, which may include stratified analyses and other related statistical methods.
- Potential Adjustments in Study Protocol or Analysis Methods: As the study may involve multiple centers, in case of ethical or administrative restrictions at certain time at specific sites, AI assistance may be temporarily suspended to approximate the scenario without AI assistance at those sites. In such cases, the corresponding results may be reported as the with-AI and without-AI phases, rather than labeling them as baseline and AI-available phases. Furthermore, radiologists who decline to provide demographic or occupational information (e.g., years of professional experience or sex)-variables that may serve as potential confounders-will be excluded from adjusted and stratified analyses that require such covariates. These approaches may need to be incorporated into confounding factor or stratified analyses, and we may update related conditions accordingly when necessary.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Radiologists with/without chest CT interpretation AI assistant | – | Device: An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretationThis study employs a single-arm, within-subjects design. A cohort of radiologists will be followed through two sequential practice phases: (1) Baseline (Control) Phase: Participants interpret and report on chest CT scans using their standard clinical workflow without AI assistance. (2) AI-available phase: The same participants interpret and report on a different set of chest CT scans with the integrated AI-assisted reporting system activated in their workflow. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study population is defined as follows: 1. Radiologists: Attending radiologists who routinely interpret chest CT scans as part of their clinical duties at the participating medical centers. 2. Chest CT Scans: Clinically indicated, non-contrast chest CT examinations acquired from the patient populations served by the participating centers. The scans are stratified into two groups based on the date of acquisition: before and after the implementation of the AI reporting system.
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 |
|---|---|---|
| An AI-assisted reporting system integrated into the clinical workflow, providing automated draft generation to assist with chest CT interpretationThe intervention under evaluation is an AI-assisted diagnostic reporting system, integrated directly into the radiologists' workflow. The system analyzes the CT images in real time using an AI model and automatically generates a structured, preliminary radiology report draft. The interpreting radiologist reviews this AI-generated draft, which is presented within their familiar reporting interface. The radiologist then actively edits, confirms, supplements, or overrides the draft content as necessary before finalizing and signing the report. This intervention is distinguished from other AI tools by its focus on end-to-end reporting efficiency via integrated draft generation within the radiologist's classic workflow. It moves beyond simple abnormality detection or highlighting by generating a complete, structured narrative report draft, aiming to reduce dictation/typing time and minimize oversight of findings. | Device | Radiologists with/without chest CT interpretation AI assistant |
Primary outcome
Change in Average Image Interpretation Time
Time frame: Time of interpretation will be collected once the data become fully available (generally within 2 weeks after the planned primary completion date). Final aggregated analysis will be completed within 3 months after the collection of potential confounders.
Comparison of the average time taken by participating radiologists to complete standard chest CT interpretation tasks, measured both with and without use of the automated interpretation tool. The time will be recorded from the start to the completion of each individual reading case.
Change in Chest CT Report Quality Score
Time frame: Reports will be distributed to external experts for scoring once the data become available, with scoring results returned within 7 days. Final aggregated analysis will be completed within 3 months after the collection of potential confounders.
Comparison of the subjective quality of chest CT reports written with and without automated tool support. Blinded external experts will evaluate the subjective quality of all sampled reports using a 10-point rating scale, with scores ranging from 1 (poorest quality) to 10 (highest quality).
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 11 Jun 2026.
- Background: PubMed 41053447Teo ZL, Thirunavukarasu AJ, Elangovan K, Cheng H, Moova P, Soetikno B, Nielsen C, Pollreisz A, Ting DSJ, Morris RJT, Shah NH, Langlotz CP, Ting DSW. Generative artificial intelligence in medicine. Nat Med. 2025 Oct;31(10):3270-3282. doi: 10.1038/s41591-025-03983-2. Epub 2025 Oct 6.
- Background: PubMed 41776077Chen SF, Alyakin A, Seas A, Yang E, Choi JJ, Lee JV, Chen AL, Warman PI, Bitolas RT, Steele RJ, Alber DA, Oermann EK. LLM-assisted systematic review of large language models in clinical medicine. Nat Med. 2026 Mar;32(3):1152-1159. doi: 10.1038/s41591-026-04229-5. Epub 2026 Mar 3.
- Individual participant dataSharing planned: No. This is an observational study analyzing aggregated, de-identified operational metrics (e.g., radiologist efficiency, report quality scores) derived from routine clinical practice. The data are not collected as part of a prospective clinical trial and are not structured for independent analysis. Findings will be disseminated through peer-reviewed publications.
Related trials for Thoracic Diseases
RecruitingPhase 3
MK-3475A±Calderasib (MK-1084) in Completely Resected Stage IIA-IIIB (N2) KRAS G12Cm NSCLC (MK-1084-013)
Non-small Cell Lung Cancer
RecruitingObservational study
Society of Thoracic Surgeons and American College of Cardiology Transcatheter Valve Therapy Registry (STS/ACC TVT Registry™): Registry Purpose and Research Objective
Aortic Valve Stenosis
RecruitingPhase 2
A Study of Navlimetostat (BMS-986504) Monotherapy and in Combination With Other Agents in Participants With Advanced and/or Metastatic Solid Tumors With Homozygous MTAP Deletion (MountainTAP-5)
Solid Tumors
Not yet recruitingPhase 2
Combination Bevacizumab and PRGN-2012 in Adults With Recurrent Respiratory Papillomatosis (RRP)
Respiratory Recurrent Papillomatosis (RRP), Human Papillomavirus (HPV)
Not yet recruitingPhase 1
A Phase 1 Trial of KAN-001 With Pembrolizumab, With or Without Chemotherapy, in Participants With PD-L1-Positive Non-Small Cell Lung Cancer
Non-Small Cell Lung Cancer
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, 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?
An observational study of Thoracic Diseases and Chest CT Scan, 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?
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: opening soon, as of the sponsor's last update on 11 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?
Change in Average Image Interpretation Time (time frame: Time of interpretation will be collected once the data become fully available (generally within 2 weeks after the planned primary completion date). Final aggregated analysis will be completed within 3 months after the collection of potential confounders.). 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?
On the ClinicalTrials.gov record for NCT07640906, which also lists 2 linked publications.
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 11 Jun 2026.