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.

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.

See arm description.

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.

Changes in self-assesed uncertainty ranging from 0 (very uncertain) to 10 (very certain) from pre- to post-intervention.

The self-assessed uncertainty of cases where the participant has clicked a "would you like to discuss this case with a collegue"-button.

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).