Human-AI Uncertainty Callibration for Improved Skin Lesion Segmentation

Copenhagen Academy for Medical Education and Simulation

Status: Not yet enrolling

Conditions: Skin Lesions

Treatments: Other: FDM; Other: Base Model

Study type: Interventional

Funder types: Other

Identifiers

NCT07468357

F-25076782

Details and patient eligibility

About

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.

Full description

A detailed description of the FDM is presented in the references.

Enrollment: 50 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Board certified dermatologists with clinical experience in dermoscopic diagnosis.

Exclusion criteria

• Doctors who have not yet finished their specialization and dermatologists.
• Dermatologists without clinical experience in dermoscopic diagnosis.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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50 participants in 2 patient groups

Base Model

Active Comparator group

Description:

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.

Treatment:

Other: Base Model

FDM

Experimental group

Description:

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.

Treatment:

Other: FDM