DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images

N/ANot Yet RecruitingNCT07823023

Beijing Friendship Hospital64 enrolled

Overview

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 Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

OTHER

Masking

SINGLE

Enrollment

Conditions

Medical Image Integrity Verification

Interventions

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.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * 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. * 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. Exclusion Criteria: * 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.

Outcomes

Primary Outcomes

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.