Towards Trustworthy AI in Dentistry.

Journal: Journal of dental research
PMID: 35746889

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.

Authors

  • 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.
  • 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 Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • F Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • W Samek
    Department of Video Coding and Analytics, Fraunhofer Heinrich Hertz Institute, Berlin, Germany.

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