1 Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.
2 Department of Ophthalmology, University of Toronto, Toronto, ON, Canada.
3 Faculty of Engineering, McGill University, Montreal, QC, Canada.
4 Division of Computer Engineering, Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada.
5 Division of Dermatology, Department of Medicine, McGill University, Montreal, QC, Canada.
6 Division of Dermatology, Department of Medicine, Jewish General Hospital, Montreal, QC, Canada.
7 Lady Davis Institute for Medical Research, Montreal, QC, Canada.

Background: Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians' awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting.

Objectives: To analyze the characteristics and trends of AI skin cancer publications from dermatology journals.

Methods: AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles' general characteristics and features related to AI.

Results: A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%).

Conclusions: Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.