[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100628954":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":32,"centralContacts":36,"locations":41,"responsibleParty":43,"collaborators":46,"id":49,"slug":50,"hasResults":51,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":41,"eligibilityCriteria":55,"healthyVolunteers":56,"sex":57,"minAge":41,"maxAge":41,"enrollmentInfo":58,"targetDuration":41,"studyType":61,"phases":62,"briefSummary":64,"conditions":65,"keywords":67,"overallStatus":75,"whyStopped":41,"lastUpdateSubmitDate":76,"lastUpdatePostDateStruct":77,"startDateStruct":80,"completionDateStruct":82,"leadSponsor":84,"locationsCount":41},{"fullName":5,"class":6},"Copenhagen Academy for Medical Education and Simulation","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Base Model","ACTIVE_COMPARATOR","The study participant is presented with a patient case including patient demographics (gender, age, placement of lesion) and two lesion images: 1 overview image, and 1 dermoscopic image. They are asked first to indicate an initial diagnosis along with their self-perceived uncertainty for this specific case before they receive Intervention 1. This initial diagnosis will act as the control. Intervention 1 is AI-generated multi-class probabilities (from a model trained on a large dataset of dermoscopic and overview images similar to the ones used for testing) and only the most likely diagnosis is presented accompanied by uncertainty estimates in percent.\n\nAfter the AI input, the study participant is given the chance to change their diagnosis and indicate any potential shift in uncertainty.",[13],"Other: Base Model",{"label":15,"type":16,"description":17,"interventionNames":18},"FDM","EXPERIMENTAL","The initial diagnosis and indication of self-perceived uncertainty follows the same procedure as for Intervention 1. Intervention 2 is the most likely diagnosis accompanied by a calibrated uncertainty generated by the FDM model (i.e. trained on the study participants previous answers + the crowd annotations on the training data + the base model prediction).\n\nAfter the AI input, the study participant is given the chance to change their diagnosis and indicate any potential shift in uncertainty.",[19],"Other: FDM",[21,26],{"type":6,"name":9,"description":22,"armGroupLabels":23,"otherNames":24},"See arm description.",[9],[25],"Intervention 1",{"type":6,"name":15,"description":27,"armGroupLabels":28,"otherNames":29},"See arm description",[15],[30,31],"Final Decision Model","Intervention 2",[33],{"name":34,"affiliation":5,"role":35},"Martin Tolsgaard, Professor","STUDY_CHAIR",[37],{"name":38,"role":39,"phone":40,"phoneExt":41,"email":42},"Julie Renata Bjerremand","CONTACT","+45 53593700",null,"julierenata@outlook.com",{"type":44,"investigatorFullName":38,"investigatorTitle":45,"investigatorAffiliation":5,"oldNameTitle":41,"oldOrganization":41},"PRINCIPAL_INVESTIGATOR","Principal Investigator",[47],{"name":48,"class":6},"Technical University of Denmark","100628954","human-ai-uncertainty-callibration-for-improved-skin-lesion-segmentation-100628954",false,"NCT07468357","Human-AI Uncertainty Callibration for Improved Skin Lesion Segmentation","The Effect of Human-AI Uncertainty Calibration vs. AI Uncertainty Alone on the Diagnostic Accuracy of Human Experts for Skin Lesions - a Randomized Controlled Trial.","Inclusion Criteria:\n\n* Board certified dermatologists with clinical experience in dermoscopic diagnosis.\n\nExclusion Criteria:\n\n* Doctors who have not yet finished their specialization and dermatologists.\n* Dermatologists without clinical experience in dermoscopic diagnosis.",true,"ALL",{"count":59,"type":60},50,"ESTIMATED","INTERVENTIONAL",[63],"NA","The goal of this randomized controlled study is to compare the effect of a new, personalized uncertainty-aware decision model (FDM) to a standard image recognition model in improving the diagnostic accuracy while reducing diagnostic uncertainty in experienced dermatologists tasked with differentiating between melanomas, moles and other benign skin lesions. The main question it aims to answer: Is the FDM a feasible method for an improved human AI partnership in which trust is build, misdiagnoses are avoided, and uncertainty is duly introduced or reduced.\n\nThe investigators expect to see only a slight increase in collective diagnostic accuracy for both interventions as the the human participants are skilled dermatologist and thus have high accuracies pre-intervention.\n\nThe investigators expect to see a higher increase in diagnostic certainty for the FDM intervention compared to the diagnostic certainty in the Base Model intervention.\n\nThe investigators expect to see a higher amount of diagnosis changes from incorrect to correct in the FDM group compared to the Base Model group.\n\nThe investigators do not expect any learning effect during the study.\n\nParticipants will start by answering a series of training cases consisting of images of skin lesions. These are used to train their individual FDM (only for the FDM-intervention group). From here, the participants will be randomized into two arms determining which of the two interventions they are exposed to. The participants will solve each case withouth any intervention first, and this reply will act as a control.",[66],"Skin Lesions",[68,69,70,71,72,73,74],"Dermatology","AI","Artificial Intelligence","Uncertainty Calibration","Dermoscopy","Bayesian Deep Learning","Human-AI Decision Model","NOT_YET_RECRUITING","2026-03-11",{"date":78,"type":79},"2026-03-12","ACTUAL",{"date":81,"type":60},"2026-03-01",{"date":83,"type":60},"2026-11",{"name":5,"class":6}]