[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"medical-image-integrity-verification\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:medical-image-integrity-verification":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":28,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":34,"lastUpdatePostDateStruct":35,"startDateStruct":38,"completionDateStruct":40,"leadSponsor":42,"locationsCount":5},"100656266","deepmedfake-assisted-detection-of-synthetic-and-discordant-medical-images-100656266",false,"NCT07823023","DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images","Evaluation of the Clinical Performance and Multi-Setting Application of an AI-Based Medical Image Verification System","Inclusion Criteria:\n\n* Aged 18 years or older.\n* Able to complete reading periods and all required electronic study procedures.\n* Completed the standardized study training and practice cases.\n* Provided written informed consent before participation.\n\n  （1）Hospital clinical cohort participants must also meet the following criteria:\n* Hold a valid physician qualification.\n* 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.\n* Have direct professional experience reviewing all imaging modalities and clinical imaging domains assigned to them in the study.\n* Routinely use the assigned medical images for image interpretation, image verification or clinical decision-making.\n\n  （2）Healthcare audit cohort participants must also meet the following criteria:\n* Currently work in healthcare audit, medical reimbursement review, medical-cost review or medical-material verification.\n* Have experience reviewing medical imaging materials as part of their routine work.\n* Have knowledge required to interpret the medical images and patient information presented in the study and to determine whether further verification is required.\n\nExclusion Criteria:\n\n* Direct involvement in development of the locked DeepMedFake model, determination of model weights or selection of decision thresholds.\n* Direct involvement in selection or construction of the formal case library or adjudication of the reference standard.\n* Direct involvement in generation or implementation of the reader randomization sequence.\n* Previous participation as a reader in the pilot study.\n* Previous access to any formal study case, case-construction record or reference-standard label.\n* Access to undisclosed study information that could permit advance identification of case type or image-generation method.\n* A financial, professional or other conflict of interest considered likely to compromise independent case assessment.",true,"ALL","18 Years",{"count":20,"type":21},64,"ESTIMATED","INTERVENTIONAL",[24],"NA","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.",[27],"Medical Image Integrity Verification",[29,30,31,32],"Artificial intelligence","Medical imaging","Synthetic medical image","Patient-information-discordant medical images","NOT_YET_RECRUITING","2026-09-12",{"date":36,"type":37},"2026-09-16","ACTUAL",{"date":39,"type":21},"2026-09-20",{"date":41,"type":21},"2026-10-31",{"name":43,"class":44},"Beijing Friendship Hospital","OTHER"]