Artificial intelligence for oral and dental healthcare: Core education curriculum

• Published in: Journal of dentistry 2023-01, Vol.128, p.104363-104363, Article 104363
• Main Authors: Schwendicke, Falk, Chaurasia, Akhilanand, Wiegand, Thomas, Uribe, Sergio E., Fontana, Margherita, Akota, Ilze, Tryfonos, Olga, Krois, Joachim
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Core curriculum; Curricula; Curriculum; Deep learning; Delivery of Health Care; Delphi method; Dental; Dental materials; Dentistry; Dentists; Domains; Education; Education, Dental; Educational objectives; Health care; Health care policy; Health Personnel; Health services; Humans; Information literacy; Literacy; Machine learning; Medical personnel; Open source software; Oral hygiene; Professionals; Teeth
• ISSN: 0300-5712; 1879-176X
• DOI: 10.1016/j.jdent.2022.104363

Artificial intelligence (AI) is swiftly entering oral health services and dentistry, while most providers show limited knowledge and skills to appraise dental AI applications. We aimed to define a core curriculum for both undergraduate and postgraduate education, establishing a minimum set of outcomes learners should acquire when taught about oral and dental AI. Existing curricula and other documents focusing on literacy of medical professionals around AI were screened and relevant items extracted. Items were scoped and adapted using expert interviews with members of the IADR's e-oral health group, the ITU/WHO's Focus Group AI for Health and the Association for Dental Education in Europe. Learning outcome levels were defined and each item assigned to a level. Items were systematized into domains and a curricular structure defined. The resulting curriculum was consented using an online Delphi process. Four domains of learning outcomes emerged, with most outcomes being on the “knowledge” level: (1) Basic definitions and terms, the reasoning behind AI and the principle of machine learning, the idea of training, validating and testing models, the definition of reference tests, the contrast between dynamic and static AI, and the problem of AI being a black box and requiring explainability should be known. (2) Use cases, the required types of AI to address them, and the typical setup of AI software for dental purposes should be taught. (3) Evaluation metrics, their interpretation, the relevant impact of AI on patient or societal health outcomes and associated examples should be considered. (4) Issues around generalizability and representativeness, explainability, autonomy and accountability and the need for governance should be highlighted. Both educators and learners should consider this core curriculum during planning, conducting and evaluating oral and dental AI education. A core curriculum on oral and dental AI may help to increase oral and dental healthcare providers’ literacy around AI, allowing them to critically appraise AI applications and to use them consciously and on an informed basis.