Best Practices for Clinical Skin Image Acquisition in Translational Artificial Intelligence Research

Recent advances in artificial intelligence research have led to an increase in the development of algorithms for detecting malignancies from clinical and dermoscopic images of skin diseases. These methods are dependent on the collection of training and testing data. There are important considerations when acquiring skin images and data for translational artificial intelligence research. In this paper, we discuss the best practices and challenges for light photography image data collection, covering ethics, image acquisition, labeling, curation, and storage. The purpose of this work is to improve artificial intelligence for malignancy detection by sup-porting intentional data collection and collaboration between subject matter experts, such as dermatologists and data scientists.Journal of Investigative Dermatology (2023) 143, 1127e1132; doi:10.1016/j.jid.2023.02.035

Automated summary

Recent developments in artificial intelligence research have led to the increased use of algorithms for detecting malignancies in clinical and dermoscopic images of skin diseases. Gathering training and testing data is crucial for these methods. This paper explores the best practices and challenges in collecting skin images and data for translational artificial intelligence research, including ethics, image acquisition, labeling, curation, and storage. The aim is to enhance malignancy detection using artificial intelligence by facilitating intentional data collection and collaboration between dermatologists and data scientists.