An interventional study of Base Model and FDM in Skin Lesions, sponsored by Copenhagen Academy for Medical Education and Simulation. Not yet recruiting. Per ClinicalTrials.gov, last updated 2026-03-12.
Sponsored by Copenhagen Academy for Medical Education and Simulation · Not applicable, Interventional, and Diagnostic
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.
The 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.
The investigators expect to see a higher increase in diagnostic certainty for the FDM intervention compared to the diagnostic certainty in the Base Model intervention.
The investigators expect to see a higher amount of diagnosis changes from incorrect to correct in the FDM group compared to the Base Model group.
The investigators do not expect any learning effect during the study.
Participants 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.
A detailed description of the FDM is presented in the references.
Exclusion Criteria:
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. After the AI input, the study participant is given the chance to change their diagnosis and indicate any potential shift in uncertainty.
Other: Base Model
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). After the AI input, the study participant is given the chance to change their diagnosis and indicate any potential shift in uncertainty.
Other: FDM
See arm description.
Also known as: Intervention 1
See arm description
Also known as: Final Decision Model, Intervention 2
Accuracy
Diagnostic accuracy in differentiating between melanoma, nevus, and benign keratosis. Defined as the percentage of correct diagnoses. Ground truth is based on histopathologically verified diagnoses.
Time frame: Immediately after the intervention.
Uncertainty
Changes in self-assesed uncertainty ranging from 0 (very uncertain) to 10 (very certain) from pre- to post-intervention.
Time frame: Immediately after the intervention.
Cut-off uncertainty
The self-assessed uncertainty of cases where the participant has clicked a "would you like to discuss this case with a collegue"-button.
Time frame: Immediately after the intervention.
Time
Time from the start to finish of each case with a split time corresponding to the end of the control phase (the time "Show AI input"-button is clicked).
Time frame: Immediately after the intervention.
No study locations are listed for this record.
This study is not yet recruiting, as verified in Mar 2026. You cannot join it, but the record below documents what was studied.
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Copenhagen Academy for Medical Education and Simulation