CE programme

Use and Impact of Artificial Intelligence in Dental Medicine: Current trends and future outlook




Michael M. Bornstein

Michael Bornstein has been appointed in January 2020 as professor and chair of the Department of Oral Health & Medicine at the University Center for Dental Medicine Basel (UZB) of the University of Basel, Switzerland.

His fields of research include cone beam computed tomography (CBCT) in clinical dental practice, diagnostic imaging, stomatology/oral medicine, GBR procedures and dental implants. He has published over 200 original articles, and is the author / co-author of numerous case reports, review articles, and book chapters.

Current trends and future outlook of Artificial Intelligence in Dental Medicine.

Artificial intelligence (AI) is defined as the capability of a machine to imitate intelligent human behavior to perform complex tasks, such as problem solving, object and word recognition, and decision-making. In the field of medicine, a large number of AI models are developing for automatic prediction of disease risk, detection of abnormalities/pathologies, diagnosis of disease, and evaluation of prognosis. Radiology is seen to offer a more straightforward access for AI into medicine due to its in nature of producing digitally coded images that can be more easily translated into computer language. In dentistry, and even more in the field of dental and maxillofacial radiology (DMFR), pre-clinical studies have been reporting on AI diagnostic models to exactly locate root canal orifices, detect vertical root fractures and proximal dental caries with generally favorable findings. These initial data were encouraging further studies on AI diagnostic models to transfer pre-clinical findings into clinical applications. Today, AI models have been described and also have been partially implemented for various applications in DMFR, which are mainly focusing on automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The diagnostic performance of the AI models varies among different algorithms used, and it is still necessary to verify the generalizability and reliability of these models prior to transferring and implementing these into clinical practice. Nevertheless, AI will certainly have a more important role to play in the future and influence other fields in dentistry. AI applications can be thus also be seen as the next step in the digital revolution in dental medicine.

Learning objectives:

  • Be able to define AI, and it’s potential impact in healthcare in general
  • Know about current applications and trends in use of AI in dentistry
  • Know about potential future trends of AI in dentistry and healthcare