RadiologyNCT07865741
AI-Based Quality Control for Radiology Reporting
A study of Radiology and Medical Errors, run by Huazhong University of Science and Technology.
Trial summary
The goal of this clinical trial is to learn whether an artificial intelligence (AI)-based radiology report quality-control system can improve the accuracy and workflow efficiency of routine radiology reporting among active radiologists at Tongji Hospital. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Radiology, Medical Errors
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 109 participants
- Where
- No sites listed yet
- Study dates
- Oct 2026to Jan 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
- Radiologist working in the Department of Radiology at Tongji Hospital.
- Participated in routine radiology reporting during the pre-intervention reference period.
- Reporting work includes at least one target imaging examination type/workflow covered by the study AI quality-control system (CT, MR, and/or CR as applicable).
- Has a valid physician identifier that permits linkage of radiology reports, workflow timestamps, and randomized study assignment.
- Able and willing to provide informed consent.
Who cannot join
- No additional exclusion criteria.
About this study
Official title: Effect of an AI-Based Radiology Report Quality-Control System on Report Accuracy and Workflow Efficiency: A Radiologist-Level Randomized Controlled Trial
The goal of this clinical trial is to learn whether an artificial intelligence (AI)-based radiology report quality-control system can improve the accuracy and workflow efficiency of routine radiology reporting among active radiologists at Tongji Hospital. The main questions it aims to answer are:
- Does access to the AI quality-control system reduce quality problems remaining in final radiology reports?
- Does access to the AI quality-control system improve reporting and reviewing efficiency?
- In the two-stage reporting workflow, do the effects of AI differ depending on whether AI is available to the reporting radiologist, the reviewing radiologist, or both?
Read more
Researchers will compare radiologists assigned to receive access to Radiology Report GPT with radiologists who continue the usual reporting workflow without access to the study AI system. Radiologists are randomized at the individual level. Because CT and MR reports commonly involve both a reporting radiologist and a reviewing radiologist, report-level analyses will classify reports according to AI availability at the two workflow stages, resulting in four combinations of reporter and reviewer AI exposure. The primary randomized evaluation will cover the first month after intervention initiation, with prespecified secondary and exploratory analyses extending through 3 months.
Participants will:
- Be assigned to either the AI quality-control group or the usual-workflow control group.
- Continue their routine radiology reporting and reviewing work during the study period.
- If assigned to the AI group, receive access to Radiology Report GPT, which automatically pre-reviews submitted radiology reports and provides quality-control alerts and suggested revisions. Radiologists will retain final decision authority over all reports.
Final-report accuracy will be assessed independently of the intervention AI system and will include human review. Additional prespecified outcomes will examine report revisions, AI interaction measures, stage-specific and cross-stage workflow effects, recommended imaging follow-up and other downstream health-care utilization, patient satisfaction, and referring-clinician satisfaction.
- Conditions
- Radiology, Medical Errors
- Being tested
- Radiology Report GPT (AI-Based Radiology Report Quality-Control System)
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Radiology Report GPT (AI-Based Radiology Report Quality-Control System)Device
Radiology Report GPT is an AI-based pre-review and quality-control system for radiology reports. After a report is submitted, the system may identify potential textual/terminology errors, patient-sex inconsistencies, formatting or numerical errors, examination-type inconsistencies, findings-impression inconsistencies, lesion location/count discrepancies, omissions between findings and impression, and missing or incorrect diagnostic classifications. When a potential issue is detected, the system presents an alert and suggested revision. The radiologist remains responsible for the final report and may accept, modify, or ignore the suggestion. The system does not automatically edit or sign the report. The exact software/model version and the trial deployment mode will be locked and documented before intervention activation.
| Study group | Role | What they receive |
|---|---|---|
| AI Quality-Control Group | Experimental | Radiologists enrolled in this arm receive access to the study AI radiology report quality-control system within the routine reporting workflow. After report submission, the system performs automated pre-review and may present alerts and suggested revisions. Radiologists retain final decision authority. Receives: Device: Radiology Report GPT (AI-Based Radiology Report Quality-Control System) |
| Usual Workflow Control Group | No intervention | Radiologists enrolled in this arm continue the existing RIS reporting and reviewing workflow without access to the study AI quality-control alerts or suggested revisions. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Presence of at Least One Confirmed Residual Quality-Control Problem in the Final Radiology Report
For each eligible final radiology report, this binary outcome indicates whether at least one confirmed prespecified residual quality-control problem is present (yes/no). The outcome-assessment process will be independent of the intervention AI system and will include human review. Prespecified categories include textual/terminology errors, sex or examination-type inconsistencies, formatting/numerical errors, findings-impression inconsistencies, lesion location/count discrepancies, omissions, and missing or incorrect diagnostic classifications.
Measured: During the first month after intervention initiation
Radiology Report Turnaround Time
Turnaround time will be measured for each eligible radiology report as the elapsed time, from completion of the imaging examination to final report approval. Both timestamps will be obtained from the hospital information systems. Lower values indicate faster completion of the radiology reporting workflow.
Measured: During the first month after intervention initiation
Secondary outcomes (2)
Inter-Report Interval
Workflow-pace proxy measured between consecutive reports completed by the same reporting radiologist on the same calendar day. This measure reflects overall reporting pace rather than active time spent on a specific report and will therefore be analyzed as a secondary outcome.
Measured: During the first month after intervention initiation
Inter-Check Interval
Workflow-pace proxy measured between consecutive report approvals completed by the same reviewing radiologist on the same calendar day. This measure reflects overall reviewing pace rather than active time spent reviewing a specific report and will therefore be analyzed as a secondary outcome.
Measured: During the first month after intervention initiation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Oct 2026
- Study startOct 2026 (estimated)
- Primary completionNov 2026 (estimated)
- Study completionJan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for RadiologyStudies 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Individual radiologists are randomized to AI quality-control access or the usual reporting workflow. The unit of randomization is the radiologist. For reports that involve a reporting and a reviewing radiologist, report-level exposure is classified by the randomized assignments of both radiologists, producing a 2×2 reporter-AI-by-reviewer-AI structure.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 109 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Luyang Song, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07865741
- Sponsor's protocol ID
- TJ-IRB202609054
- Status last verified
- Oct 2026
- First submitted
- 3 Oct 2026
- Last updated
- 13 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of an AI-Based Radiology Report Quality-Control System on Report Accuracy and Workflow Efficiency: A Radiologist-Level Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07865741
- Acronym / sponsor ID
- TJ-IRB202609054
- Conditions
- Radiology, Medical Errors
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 109 participants (estimated)
- Lead sponsor
- Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- Oct 2026 (estimated)
- Primary completion
- Nov 2026 (estimated)
- Study completion
- Jan 2027 (estimated)
- First posted
- 8 Oct 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 clinical trial is to learn whether an artificial intelligence (AI)-based radiology report quality-control system can improve the accuracy and workflow efficiency of routine radiology reporting among active radiologists at Tongji Hospital. The main questions it aims to answer are:
- Does access to the AI quality-control system reduce quality problems remaining in final radiology reports?
- Does access to the AI quality-control system improve reporting and reviewing efficiency?
- In the two-stage reporting workflow, do the effects of AI differ depending on whether AI is available to the reporting radiologist, the reviewing radiologist, or both?
Researchers will compare radiologists assigned to receive access to Radiology Report GPT with radiologists who continue the usual reporting workflow without access to the study AI system. Radiologists are randomized at the individual level. Because CT and MR reports commonly involve both a reporting radiologist and a reviewing radiologist, report-level analyses will classify reports according to AI availability at the two workflow stages, resulting in four combinations of reporter and reviewer AI exposure. The primary randomized evaluation will cover the first month after intervention initiation, with prespecified secondary and exploratory analyses extending through 3 months.
Participants will:
- Be assigned to either the AI quality-control group or the usual-workflow control group.
- Continue their routine radiology reporting and reviewing work during the study period.
- If assigned to the AI group, receive access to Radiology Report GPT, which automatically pre-reviews submitted radiology reports and provides quality-control alerts and suggested revisions. Radiologists will retain final decision authority over all reports.
Final-report accuracy will be assessed independently of the intervention AI system and will include human review. Additional prespecified outcomes will examine report revisions, AI interaction measures, stage-specific and cross-stage workflow effects, recommended imaging follow-up and other downstream health-care utilization, patient satisfaction, and referring-clinician satisfaction.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- Individual radiologists are randomized to AI quality-control access or the usual reporting workflow. The unit of randomization is the radiologist. For reports that involve a reporting and a reviewing radiologist, report-level exposure is classified by the randomized assignments of both radiologists, producing a 2×2 reporter-AI-by-reviewer-AI structure.
| Arm | Type | Receives |
|---|---|---|
| AI Quality-Control Group | Experimental | Device: Radiology Report GPT (AI-Based Radiology Report Quality-Control System)Radiologists enrolled in this arm receive access to the study AI radiology report quality-control system within the routine reporting workflow. After report submission, the system performs automated pre-review and may present alerts and suggested revisions. Radiologists retain final decision authority. |
| Usual Workflow Control Group | No intervention | –Radiologists enrolled in this arm continue the existing RIS reporting and reviewing workflow without access to the study AI quality-control alerts or suggested revisions. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Radiologist working in the Department of Radiology at Tongji Hospital.
- Participated in routine radiology reporting during the pre-intervention reference period.
- Reporting work includes at least one target imaging examination type/workflow covered by the study AI quality-control system (CT, MR, and/or CR as applicable).
- Has a valid physician identifier that permits linkage of radiology reports, workflow timestamps, and randomized study assignment.
- Able and willing to provide informed consent.
Key exclusion criteria (1 listed)
- No additional exclusion criteria.
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 |
|---|---|---|
| Radiology Report GPT (AI-Based Radiology Report Quality-Control System)Radiology Report GPT is an AI-based pre-review and quality-control system for radiology reports. After a report is submitted, the system may identify potential textual/terminology errors, patient-sex inconsistencies, formatting or numerical errors, examination-type inconsistencies, findings-impression inconsistencies, lesion location/count discrepancies, omissions between findings and impression, and missing or incorrect diagnostic classifications. When a potential issue is detected, the system presents an alert and suggested revision. The radiologist remains responsible for the final report and may accept, modify, or ignore the suggestion. The system does not automatically edit or sign the report. The exact software/model version and the trial deployment mode will be locked and documented before intervention activation. | Device | AI Quality-Control Group |
Primary outcome
Presence of at Least One Confirmed Residual Quality-Control Problem in the Final Radiology Report
Time frame: During the first month after intervention initiation
For each eligible final radiology report, this binary outcome indicates whether at least one confirmed prespecified residual quality-control problem is present (yes/no). The outcome-assessment process will be independent of the intervention AI system and will include human review. Prespecified categories include textual/terminology errors, sex or examination-type inconsistencies, formatting/numerical errors, findings-impression inconsistencies, lesion location/count discrepancies, omissions, and missing or incorrect diagnostic classifications.
Radiology Report Turnaround Time
Time frame: During the first month after intervention initiation
Turnaround time will be measured for each eligible radiology report as the elapsed time, from completion of the imaging examination to final report approval. Both timestamps will be obtained from the hospital information systems. Lower values indicate faster completion of the radiology reporting workflow.
2 secondary outcomes
Inter-Report Interval
Time frame: During the first month after intervention initiation
Inter-Check Interval
Time frame: During the first month after intervention initiation
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 13 Oct 2026.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study of Radiology and Medical Errors, run by Huazhong University of Science and Technology.
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 5 inclusion and 1 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: opening soon, as of the sponsor's last update on 13 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Presence of at Least One Confirmed Residual Quality-Control Problem in the Final Radiology Report (time frame: During the first month after intervention initiation). 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 13 Oct 2026.