Medical Image Integrity VerificationNCT07823023
DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images
A study testing DeepMedFake in people with Medical Image Integrity Verification, run by Beijing Friendship Hospital at 1 site in China.
Trial summary
This prospective randomized controlled trial will evaluate whether DeepMedFake improves the identification of medical images that require further verification. DeepMedFake is an artificial intelligence-based medical image analysis system that provides an image-authenticity risk assessment, a patient-image consistency… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Medical Image Integrity Verification
- What is being tested?
- DeepMedFake
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 64 participants
- Where
- 1 siteChina
- Study dates
- 20 Sep 2026to 31 Oct 2026
- 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
- Aged 18 years or older.
- Able to complete reading periods and all required electronic study procedures.
- Completed the standardized study training and practice cases.
- Provided written informed consent before participation.
- （1）Hospital clinical cohort participants must also meet the following criteria:
- Hold a valid physician qualification.
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- Currently practice in ophthalmology, radiology, ultrasound medicine, or a clinical specialty in which imaging of the neurological or cerebrovascular, cardiovascular, thoracic, abdominal, or musculoskeletal domain is routinely reviewed.
- Have direct professional experience reviewing all imaging modalities and clinical imaging domains assigned to them in the study.
- Routinely use the assigned medical images for image interpretation, image verification or clinical decision-making.
- （2）Healthcare audit cohort participants must also meet the following criteria:
- Currently work in healthcare audit, medical reimbursement review, medical-cost review or medical-material verification.
- Have experience reviewing medical imaging materials as part of their routine work.
- Have knowledge required to interpret the medical images and patient information presented in the study and to determine whether further verification is required.
Who cannot join
- Direct involvement in development of the locked DeepMedFake model, determination of model weights or selection of decision thresholds.
- Direct involvement in selection or construction of the formal case library or adjudication of the reference standard.
- Direct involvement in generation or implementation of the reader randomization sequence.
- Previous participation as a reader in the pilot study.
- Previous access to any formal study case, case-construction record or reference-standard label.
- Access to undisclosed study information that could permit advance identification of case type or image-generation method.
- A financial, professional or other conflict of interest considered likely to compromise independent case assessment.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Beijing Friendship Hospital, Capital Medical University
Beijing, Beijing Municipality, 100050
Han Lv, Professor, +8613901073227, chrislvhan@126.com
About this study
Official title: Evaluation of the Clinical Performance and Multi-Setting Application of an AI-Based Medical Image Verification System
This prospective randomized controlled trial will evaluate whether DeepMedFake improves the identification of medical images that require further verification. DeepMedFake is an artificial intelligence-based medical image analysis system that provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. Hospital physicians and healthcare audit professionals will participate in a randomized two-period crossover design. Each participant will assess one case set with DeepMedFake assistance and the other without AI assistance. The primary outcome in each cohort is the sensitivity of the final verification decision, evaluated separately in the hospital clinical and healthcare audit cohorts.
- Conditions
- Medical Image Integrity Verification
- Being tested
- DeepMedFake
- Keywords
- Artificial intelligence, Medical imaging, Synthetic medical image, Patient-information-discordant medical images
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
DeepMedFakeDevice
DeepMedFake is an artificial intelligence-based medical image analysis system designed to support the detection of synthetic medical images and authentic medical images paired with discordant patient information. It provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. In this study, DeepMedFake is used as a decision-support tool to assist participants in determining whether the image requires further verification before downstream clinical or healthcare audit use. Final judgments and verification decisions are made by the participants.
| Study group | Role | What they receive |
|---|---|---|
| Crossover Sequence A | Experimental | Participants undergo unassisted review of case set S1 during Period 1, followed by DeepMedFake-assisted review of case set S2 during Period 2. Receives: Device: DeepMedFake |
| Crossover Sequence B | Experimental | Participants undergo DeepMedFake-assisted review of case set S1 during Period 1, followed by unassisted review of case set S2 during Period 2. Receives: Device: DeepMedFake |
| Crossover Sequence C | Experimental | Participants undergo unassisted review of case set S2 during Period 1, followed by DeepMedFake-assisted review of case set S1 during Period 2. Receives: Device: DeepMedFake |
| Crossover Sequence D | Experimental | Participants undergo DeepMedFake-assisted review of case set S2 during Period 1, followed by unassisted review of case set S1 during Period 2. Receives: Device: DeepMedFake |
What is being measured
The primary outcome is the main question the study is designed to answer.
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
The percentage of reference-standard abnormal cases assigned to further verification by hospital physicians will be estimated under DeepMedFake-assisted and unassisted review. The effect measure is the absolute percentage-point difference between review conditions.
Measured: During both reading periods, up to 4 weeks after randomization
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Healthcare Audit Cohort
The percentage of reference-standard abnormal cases assigned to further verification by healthcare audit professionals will be estimated under DeepMedFake-assisted and unassisted review. The effect measure is the absolute percentage-point difference between review conditions.
Measured: During both reading periods, up to 4 weeks after randomization
Secondary outcomes (9)
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
The percentage of authentic, information-matched cases assigned to no further verification by hospital physicians will be estimated under each review condition and compared between conditions.
Measured: During both reading periods, up to 4 weeks after randomization
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Healthcare Audit Cohort
The percentage of authentic, information-matched cases assigned to no further verification by healthcare audit professionals will be estimated under each review condition and compared between conditions.
Measured: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Final Verification Decision for Synthetic Medical Image Cases
The percentage of reference-standard synthetic-image cases assigned to further verification will be estimated under each review condition. DeepMedFake-assisted and unassisted review will be compared separately within the hospital clinical and healthcare audit cohorts.
Measured: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Final Verification Decision in Discordant Cases
The percentage of authentic images paired with discordant patient information that are assigned to further verification will be estimated under each review condition. DeepMedFake-assisted and unassisted review will be compared separately within the hospital clinical and healthcare audit cohorts.
Measured: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Image-Authenticity Judgment
The percentage of reference-standard synthetic-image cases classified as suspected synthetic images will be estimated under each review condition and compared separately within each reader cohort.
Measured: During both reading periods, up to 4 weeks after randomization
Specificity of the Image-Authenticity Judgment
The percentage of reference-standard authentic-image cases classified as not suspected to be synthetic will be estimated under each review condition and compared separately within each reader cohort.
Measured: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Patient-Image Consistency Judgment
Among authentic images, the percentage of discordant cases classified as discordant with the displayed patient information will be estimated under each review condition and compared separately within each reader cohort. Synthetic-image cases will be excluded from this analysis.
Measured: During both reading periods, up to 4 weeks after randomization
Specificity of the Patient-Image Consistency Judgment
Among authentic images, the percentage of information-matched cases classified as matching the displayed patient information will be estimated under each review condition and compared separately within each reader cohort. Synthetic-image cases will be excluded from this analysis.
Measured: During both reading periods, up to 4 weeks after randomization
Case-Level Workflow Time
Elapsed time in seconds from successful case loading to submission of the final required case-level response, including selection of a proposed first verification pathway when required. Recorded pauses will be excluded. Workflow time will be compared between review conditions separately within each reader cohort.
Measured: During both reading periods, up to 4 weeks after randomization
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Sep 2026
- Study start20 Sep 2026 (estimated)
- Primary completion20 Oct 2026 (estimated)
- Study completion31 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Medical Image Integrity VerificationStudies of the same condition, open and under way.
- More trials run by Beijing Friendship 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 64 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beijing Friendship Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lv, Han, Beijing Friendship Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07823023
- Sponsor's protocol ID
- 2026-P2-308-01
- Status last verified
- Sep 2026
- First submitted
- 12 Sep 2026
- Last updated
- 16 Sep 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 16 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Clinical Performance and Multi-Setting Application of an AI-Based Medical Image Verification System
- ClinicalTrials.gov ID
- NCT07823023
- Acronym / sponsor ID
- 2026-P2-308-01
- Conditions
- Medical Image Integrity Verification
- Keywords
- Artificial intelligence, Medical imaging, Synthetic medical image, Patient-information-discordant medical images
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 64 participants (estimated)
- Lead sponsor
- Beijing Friendship HospitalUniversity, hospital or other
- Study start
- 20 Sep 2026 (estimated)
- Primary completion
- 20 Oct 2026 (estimated)
- Study completion
- 31 Oct 2026 (estimated)
- First posted
- 16 Sep 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 prospective randomized controlled trial will evaluate whether DeepMedFake improves the identification of medical images that require further verification. DeepMedFake is an artificial intelligence-based medical image analysis system that provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. Hospital physicians and healthcare audit professionals will participate in a randomized two-period crossover design. Each participant will assess one case set with DeepMedFake assistance and the other without AI assistance. The primary outcome in each cohort is the sensitivity of the final verification decision, evaluated separately in the hospital clinical and healthcare audit cohorts.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Crossover Sequence A | Experimental | Device: DeepMedFakeParticipants undergo unassisted review of case set S1 during Period 1, followed by DeepMedFake-assisted review of case set S2 during Period 2. |
| Crossover Sequence B | Experimental | Device: DeepMedFakeParticipants undergo DeepMedFake-assisted review of case set S1 during Period 1, followed by unassisted review of case set S2 during Period 2. |
| Crossover Sequence C | Experimental | Device: DeepMedFakeParticipants undergo unassisted review of case set S2 during Period 1, followed by DeepMedFake-assisted review of case set S1 during Period 2. |
| Crossover Sequence D | Experimental | Device: DeepMedFakeParticipants undergo DeepMedFake-assisted review of case set S2 during Period 1, followed by unassisted review of case set S1 during Period 2. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (13 listed)
- Aged 18 years or older.
- Able to complete reading periods and all required electronic study procedures.
- Completed the standardized study training and practice cases.
- Provided written informed consent before participation.
- Hold a valid physician qualification.
Key exclusion criteria (7 listed)
- Direct involvement in development of the locked DeepMedFake model, determination of model weights or selection of decision thresholds.
- Direct involvement in selection or construction of the formal case library or adjudication of the reference standard.
- Direct involvement in generation or implementation of the reader randomization sequence.
- Previous participation as a reader in the pilot study.
- Previous access to any formal study case, case-construction record or reference-standard label.
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 |
|---|---|---|
| DeepMedFakeDeepMedFake is an artificial intelligence-based medical image analysis system designed to support the detection of synthetic medical images and authentic medical images paired with discordant patient information. It provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. In this study, DeepMedFake is used as a decision-support tool to assist participants in determining whether the image requires further verification before downstream clinical or healthcare audit use. Final judgments and verification decisions are made by the participants. | Device | Crossover Sequence A, Crossover Sequence B, Crossover Sequence C, Crossover Sequence D |
Primary outcome
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
Time frame: During both reading periods, up to 4 weeks after randomization
The percentage of reference-standard abnormal cases assigned to further verification by hospital physicians will be estimated under DeepMedFake-assisted and unassisted review. The effect measure is the absolute percentage-point difference between review conditions.
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Healthcare Audit Cohort
Time frame: During both reading periods, up to 4 weeks after randomization
The percentage of reference-standard abnormal cases assigned to further verification by healthcare audit professionals will be estimated under DeepMedFake-assisted and unassisted review. The effect measure is the absolute percentage-point difference between review conditions.
9 secondary outcomes
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
Time frame: During both reading periods, up to 4 weeks after randomization
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Healthcare Audit Cohort
Time frame: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Final Verification Decision for Synthetic Medical Image Cases
Time frame: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Final Verification Decision in Discordant Cases
Time frame: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Image-Authenticity Judgment
Time frame: During both reading periods, up to 4 weeks after randomization
Specificity of the Image-Authenticity Judgment
Time frame: During both reading periods, up to 4 weeks after randomization
Sensitivity of the Patient-Image Consistency Judgment
Time frame: During both reading periods, up to 4 weeks after randomization
Specificity of the Patient-Image Consistency Judgment
Time frame: During both reading periods, up to 4 weeks after randomization
Case-Level Workflow Time
Time frame: During both reading periods, up to 4 weeks after randomization
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 16 Sep 2026.
- Individual participant dataSharing planned: No
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing DeepMedFake in people with Medical Image Integrity Verification, run by Beijing Friendship Hospital at 1 site 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 13 inclusion and 7 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 16 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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 Han Lv (+8613901073227, chrislvhan@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort (time frame: During both reading periods, up to 4 weeks after randomization). 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 16 Sep 2026.