AI trained to spot fake medical images put to the test

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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.