Table 1.

Ongoing and recently completed research regarding the use of AI-based technology in NMSC diagnosis. Information was obtained from clinicaltrials.gov.

Study Name	Study Goal	Patient Population	Design	Clinicaltrials.gov Identifier
Effectiveness of an Image Analyzing Algorithm (DERM) to Diagnose Non-melanoma Skin Cancer (NMSC) and Benign Skin Lesions Compared to Gold Standard Clinical and Histological Diagnosis	To establish how well the device DERM (Deep Ensemble for the Recognition of Malignancy, an AI-based diagnosis support tool) determines the presence of non-melanoma skin cancer in images of skin lesions collected in a clinical setting.	Patients attending a dermatology clinic with at least one suspicious skin lesion.	Prospective cohort study (actual enrollment: 572 participants)	NCT04116983
Impact of an Artificial Intelligence Platform (DERM) on the Healthcare Resource Utilization (HRU) Needed to Diagnose Skin Cancer When Used as Part of a United Kingdom-based Teledermatology Service	To establish whether the use of DERM (Deep Ensemble for the Recognition of Malignancy) in the patient pathway could reduce the number of unnecessary referrals for dermatologist review and/or biopsy.	Adult patients undergoing medical photography for imaging of at least one suspicious skin lesion.	Prospective cohort study (actual enrollment: 700 participants)	NCT04123678
Teledermoscopy and Artificial Intelligence: Effects of Implementation in Clinical Practice	To investigate the effects of utilizing teledermoscopy in clinical practice, including changes in referral patterns and effects on diagnostic accuracy. It will also investigate how to introduce AI within teledermoscopy.	Patients aged 15 years or older with a skin lesion that is assessed by a physician who are subsequently referred for teledermoscopy.	Prospective cohort study (estimated enrollment: 8000 participants)	NCT05033678