  The incidence rates of melanoma, the deadliest form of skin cancer, have been
increasing steadily worldwide, presenting a significant challenge to
dermatologists. Early detection of melanoma is crucial for improving patient
survival rates, but identifying suspicious lesions through ugly duckling (UD)
screening, the current method used for skin cancer screening, can be
challenging and often requires expertise in pigmented lesions. To address these
challenges and improve patient outcomes, an artificial intelligence (AI)
decision support tool was developed to assist dermatologists in identifying UD
from wide-field patient images. The tool uses a state-of-the-art object
detection algorithm to identify and extract all skin lesions from patient
images, which are then sorted by suspiciousness using a self-supervised AI
algorithm. A clinical validation study was conducted to evaluate the tool's
performance, which demonstrated an average sensitivity of 93% for the top-10
AI-identified UDs on skin lesions selected by the majority of experts in
pigmented skin lesions. The study also found that dermatologists confidence
increased, and the average majority agreement with the top-10 AI-identified UDs
improved to 100% when assisted by AI. The development of this AI decision
support tool aims to address the shortage of specialists, enable at-risk
patients to receive faster consultations and understand the impact of
AI-assisted screening. The tool's automation can assist dermatologists in
identifying suspicious lesions and provide a more objective assessment,
reducing subjectivity in the screening process. The future steps for this
project include expanding the dataset to include histologically confirmed
melanoma cases and increasing the number of participants for clinical
validation to strengthen the tool's reliability and adapt it for real-world
consultation.
