Towards Trustworthy AI in Dentistry. (October 2022)

• Record Type: Journal Article
• Authors: Ma, J.; Schneider, L.; Lapuschkin, S.; Achtibat, R.; Duchrau, M.; Krois, J.; Schwendicke, F.; Samek, W.
• Is Part Of: Journal of dental research. Volume 101:Number 11(2022)
• Page Start: 1263
• Page End: 1268
• Publication Date: 2022-10
• Subjects: computer vision/convolutional neural networks -- artificial intelligence -- deep learning/machine learning -- dental informatics/bioinformatics -- mathematical modeling -- standardization; Dentistry -- Periodicals; Dentistry -- Social aspects -- Periodicals; Dentistry -- Periodicals; Research -- Periodicals; 617.6005
• DOI: 10.1177/00220345221106086
• Languages: English
• ISSNs: 0022-0345

Abstract

Medical and dental artificial intelligence (AI) require the trust of both users and recipients of the AI to enhance implementation, acceptability, reach, and maintenance. Standardization is one strategy to generate such trust, with quality standards pushing for improvements in AI and reliable quality in a number of attributes. In the present brief review, we summarize ongoing activities from research and standardization that contribute to the trustworthiness of medical and, specifically, dental AI and discuss the role of standardization and some of its key elements. Furthermore, we discuss how explainable AI methods can support the development of trustworthy AI models in dentistry. In particular, we demonstrate the practical benefits of using explainable AI on the use case of caries prediction on near-infrared light transillumination images.