Towards Trustworthy AI in Dentistry

被引：33

作者：

Ma, J. ^{[1
]}

Schneider, L. ^{[2
,3
]}

Lapuschkin, S. ^{[1
]}

Achtibat, R. ^{[1
]}

Duchrau, M. ^{[2
]}

Krois, J. ^{[2
,3
]}

Schwendicke, F. ^{[2
,3
]}

Samek, W. ^{[1
,4
]}

机构：

[1] Fraunhofer Heinrich Hertz Inst, Dept Artificial Intelligence, Berlin, Germany

[2] Charite, Dept Oral Diagnost Digital Hlth & Hlth Serv Res, Assmannshauser Str 4-6, D-14197 Berlin, Germany

[3] ITU WHO Focus Grp AI Hlth, Top Grp Dent Diagnost & Digital Dent, Geneva, Switzerland

[4] BIFOLD Berlin Inst Fdn Learning & Data, Berlin, Germany

来源：

JOURNAL OF DENTAL RESEARCH | 2022年 / 101卷 / 11期

关键词：

computer vision; convolutional neural networks; artificial intelligence; deep learning; machine learning; dental informatics; bioinformatics; mathematical modeling; standardization;

D O I：

10.1177/00220345221106086

中图分类号：

R78 [口腔科学];

学科分类号：

1003 ;

摘要：

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.

引用

页码：1263 / 1268

页数：6

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