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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.
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Affiliation(s)
- J Ma
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - L Schneider
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany.,ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - S Lapuschkin
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - R Achtibat
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - M Duchrau
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - J Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany.,ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - F Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany.,ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - W Samek
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany.,BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
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