Data Foundations for Trustworthy AI in Dentistry

Sergio E. Uribe; Falk Schwendicke

doi:10.1007/978-3-031-84047-0_10

Data Foundations for Trustworthy AI in Dentistry

Sergio E. Uribe, Falk Schwendicke

Department of Conservative Dentistry and Oral Health

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Artificial intelligence (AI) applications rely heavily on the quality and management of data used to train the AI models. This chapter discusses the importance of data for dental AI, starting from the foundational equation of computer processing, AI algorithms, and data. It emphasizes the role of data quality and provides an introduction to dataset preparation, including annotations, partitioning, size considerations, sources, and potential biases. As the demand for data grows, and also the privacy concerns, the chapter introduces federated learning as a novel approach to AI algorithm training preserving data privacy. Central to this discussion is the adoption of the FAIR (Findable, Accesible, Interoperable and Reusable) data principles in dentistry, allowing trustworthy and transparent AI applications.

Original language	English
Title of host publication	Artificial Intelligence for Oral Health Care
Subtitle of host publication	Applications and Future Prospects
Editors	Falk Schwendicke, Prabhat Kumar Chaudhari, Kunaal Dhingra, Sergio E. Uribe, Manal Hamdan
Place of Publication	Cham
Publisher	Springer International Publishing
Chapter	10
Pages	151-163
Number of pages	13
Edition	1
ISBN (Electronic)	978-3-031-84047-0
ISBN (Print)	978-3-031-84046-3, 978-3-031-84049-4
DOIs	https://doi.org/10.1007/978-3-031-84047-0_10
Publication status	Published - Apr 2025

Keywords*

artificial intelligence
data managment

Field of Science*

3.2 Clinical medicine

Publication Type*

3.2. Articles or chapters in other proceedings other than those included in 3.1., with an ISBN or ISSN code

Access to Document

10.1007/978-3-031-84047-0_10

https://link.springer.com/chapter/10.1007/978-3-031-84047-0_10

Cite this

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title = "Data Foundations for Trustworthy AI in Dentistry",

abstract = "Artificial intelligence (AI) applications rely heavily on the quality and management of data used to train the AI models. This chapter discusses the importance of data for dental AI, starting from the foundational equation of computer processing, AI algorithms, and data. It emphasizes the role of data quality and provides an introduction to dataset preparation, including annotations, partitioning, size considerations, sources, and potential biases. As the demand for data grows, and also the privacy concerns, the chapter introduces federated learning as a novel approach to AI algorithm training preserving data privacy. Central to this discussion is the adoption of the FAIR (Findable, Accesible, Interoperable and Reusable) data principles in dentistry, allowing trustworthy and transparent AI applications.",

keywords = "artificial intelligence, data managment",

author = "Uribe, {Sergio E.} and Falk Schwendicke",

year = "2025",

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doi = "10.1007/978-3-031-84047-0_10",

language = "English",

isbn = "978-3-031-84046-3",

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editor = "Falk Schwendicke and Chaudhari, {Prabhat Kumar} and Kunaal Dhingra and Uribe, {Sergio E. } and Manal Hamdan",

booktitle = "Artificial Intelligence for Oral Health Care",

publisher = "Springer International Publishing",

address = "Switzerland",

edition = "1",

}

Data Foundations for Trustworthy AI in Dentistry. / Uribe, Sergio E.; Schwendicke, Falk.
Artificial Intelligence for Oral Health Care: Applications and Future Prospects. ed. / Falk Schwendicke; Prabhat Kumar Chaudhari; Kunaal Dhingra; Sergio E. Uribe; Manal Hamdan. 1. ed. Cham: Springer International Publishing, 2025. p. 151-163.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Data Foundations for Trustworthy AI in Dentistry

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AU - Schwendicke, Falk

PY - 2025/4

Y1 - 2025/4

N2 - Artificial intelligence (AI) applications rely heavily on the quality and management of data used to train the AI models. This chapter discusses the importance of data for dental AI, starting from the foundational equation of computer processing, AI algorithms, and data. It emphasizes the role of data quality and provides an introduction to dataset preparation, including annotations, partitioning, size considerations, sources, and potential biases. As the demand for data grows, and also the privacy concerns, the chapter introduces federated learning as a novel approach to AI algorithm training preserving data privacy. Central to this discussion is the adoption of the FAIR (Findable, Accesible, Interoperable and Reusable) data principles in dentistry, allowing trustworthy and transparent AI applications.

AB - Artificial intelligence (AI) applications rely heavily on the quality and management of data used to train the AI models. This chapter discusses the importance of data for dental AI, starting from the foundational equation of computer processing, AI algorithms, and data. It emphasizes the role of data quality and provides an introduction to dataset preparation, including annotations, partitioning, size considerations, sources, and potential biases. As the demand for data grows, and also the privacy concerns, the chapter introduces federated learning as a novel approach to AI algorithm training preserving data privacy. Central to this discussion is the adoption of the FAIR (Findable, Accesible, Interoperable and Reusable) data principles in dentistry, allowing trustworthy and transparent AI applications.

KW - artificial intelligence

KW - data managment

U2 - 10.1007/978-3-031-84047-0_10

DO - 10.1007/978-3-031-84047-0_10

M3 - Chapter

SN - 978-3-031-84046-3

SN - 978-3-031-84049-4

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BT - Artificial Intelligence for Oral Health Care

A2 - Schwendicke, Falk

A2 - Chaudhari, Prabhat Kumar

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A2 - Hamdan, Manal

PB - Springer International Publishing

CY - Cham

ER -

Data Foundations for Trustworthy AI in Dentistry

Abstract

Keywords*

Field of Science*

Publication Type*

Access to Document

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Cite this