Artificial Intelligence Augmented Training in Skin Cancer Diagnostics for General Practitioners

Background: The worldwide incidence of skin cancer has been rising for 50 years, in particular the incidence of malignant melanoma has increased approx. 2-7% annually and is the most common cancer amongst Danes aged 15-34. Currently there is a significant amount of misdiagnosis of skin cancer and mole cancer. Our aim is to improve general practitioners' diagnostic skills and accuracy of skin and mole cancer. Research questions: In a population of Danish General Practitioners (GPs) what is the dose/response effect of hours spent with an educational platform that offers AI augmented training and clinical feedback on their diagnostic accuracy and accurate clinical management (treatment, dismissal, referral)? Does access to an educational platform that offers AI augmented training and clinical feedback increase the number of malignant skin lesions referred by Danish GPs without simultaneously increasing the number of incorrect benign referrals? Can the participating GPs clinical accuracy be predicted from the MCQ-score by comparing their quiz answers and diagnostic accuracy on their registered lesions with their score on the MCQ? Method: 90 Danish GPs will at baseline, 1 month and end of trial answer a Multiple Choice Questionnaire (MCQ). There is no change to current clinical practice, but all participating doctors will be asked to register a clinical picture and a dermoscopic image as well as basic information about the lesion and patient (age, gender, location and diagnosis) of all skin lesions examined due to a suspicion for non-melanoma or melanoma skin cancer, raised by the GP or patient. GPs in the intervention group are besides the registration application (R-app) given access to an AI augmented training and clinical feedback through an educational smartphone app (E-app). Within the E-app the doctor can access quizzes on a library of 10,000+ skin lesions, written articles about the 40 most common skin lesions, and a clinical feedback module that gives the