Work

Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations

Year: 2022

Type: review

Abstract: Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks. Furthermore, the review summarizes the current landscape of image datasets, transfer learning approaches, challenges, and limitations within current AI literature and current regulatory pathways for approval of models as clinical decision support tools.

Source: JID Innovations

DOI: 10.1016/j.xjidi.2022.100150

Authors: Hyeon Ki Jeong, Christine Park, Ricardo Henao, Meenal Kheterpal

Institutions: Duke University, Duke Medical Center

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,000