AI in Dentistry: What to expect and when
About the Author:
Dr. Markus Blatz is Professor of Restorative Dentistry, Chairman of the Department of Preventive & Restorative Sciences and Assistant Dean for Digital Innovation & Professional Development at the University of Pennsylvania School of Dental Medicine.
AI is relatively a new topic to the dental conversation. At this point, there are general expectations that it will have a pronounced impact, but we don’t really know what that means or when that will be. I’m hopeful that the Dental AI Council will answer those questions. I have my own suspicions of what those answers may be, however.
To the question of “when?”, I submit my experience working with our field’s future flag bearers. The past several years have seen increasing numbers of digital technologies incorporated into the dental instruction taking place around the world. At my own institution, the University of Pennsylvania School of Dental Medicine, we have used virtual simulation systems for many years. These systems allow real-time tracking and evaluation of students as they are conducting teeth preparations on models and allow faculty to evaluate students’ capabilities or understand how quickly they are learning. More recently, we have started to employ haptics technologies, which allow students to simulate tooth drilling in a three-dimensional virtual environment with highly accurate tactile feedback and without any physical models or plastic teeth. In our clinical settings, students are submitting their work via intraoral optical scanners to CAD/CAM systems, which have the ability to provide computerized evaluations of tooth preparations. Such evaluations limit possible bias or subjectivity by the instructor.
As I write this, almost all indirect fixed restorations provided to our patients, such as onlays, crowns, and bridges, are fabricated through either dental-laboratory-based or chairside CAD/CAM systems. While some practitioners and faculty who were trained in a predominantly analogue environment tend to embrace these sorts of new technologies more reluctantly, the students universally welcome them. Using these digital tools is second nature to them and is commensurate with their experiences using technology in other facets of their lives, as well as with their expectations of advanced dental medicine. Based on my experience integrating new technologies into student education and clinical care, I am convinced that our field’s next generation will strongly embrace AI in their work as dentists.
In my work chairing our dental school’s largest clinical department and as assistant dean for digital innovation, the technologies I’ve seen deployed most successfully are those that help to improve patient care. For that reason, I anticipate that improved patient care will be AI’s likeliest high-level benefit––starting with diagnostics, through automated radiographic interpretation, and proceeding to treatment planning, smile design, and restoration fabrication.
From the perspective of my specialty, I expect AI systems to offer better and more accurate diagnostics, independent of the experience level, knowledge base, and possible bias of the practitioner. Offering automated CBCT analysis will allow us to make highly customized, individualized treatment plans in more consistent and predictable ways. And, while the tools we currently have allow us to perform better quality control than ever before, I expect AI will further raise quality standards, just as it has in many other industries.
Ultimately, AI’s impact will extend to more advanced aspects of patient care, including fully automated smile design, higher quality, more durable restoration and so on. It will also provide the opportunity to bridge skill gaps that exist between dentists. For example, some dentists may not be well versed in aesthetic dentistry and some patients may not have access to quality dentists with expertise in the aesthetic field. AI can close the knowledge and skill gap and increase patient access to quality care by optimizing digital smile design tools and automating procedure and restoration planning.
The scope of AI’s impact on patient care will become particularly clear, however, as machine learning radiographic diagnostic tools begin to be applied across large patient healthcare databases. Merged with general health data, great insights can be gained through consideration of population-wide oral pathologic incidence and prevalence. Study of the efficacy of preventive measures on a vast scale can help us to identify previously unknown precursors to oral health problems. More importantly, they will allow us to target at-risk populations with effective intervention.
This ability to cross-reference dental healthcare data against broader patient health data will, ultimately, open the door to discovery of correlations between oral and general health. And, while I eagerly anticipate the technology’s near-term impact on patient care, it may be AI’s potential to securely establish dental healthcare as a critical part of human health that excites me most.
