Artificial Intelligence in Dentistry: Chances and Challenges

Abstract

The term "artificial intelligence" (AI) refers to the idea of machines being capable of performing human tasks. A subdomain of AI is machine learning (ML), which "learns" intrinsic statistical patterns in data to eventually cast predictions on unseen data. Deep learning is a ML technique using multi-layer mathematical operations for learning and inferring on complex data like imagery. This succinct narrative review describes the application, limitations and possible future of AI-based dental diagnostics, treatment planning, and conduct, for example, image analysis, prediction making, record keeping, as well as dental research and discovery. AI-based applications will streamline care, relieving the dental workforce from laborious routine tasks, increasing health at lower costs for a broader population, and eventually facilitate personalized, predictive, preventive, and participatory dentistry. However, AI solutions have not by large entered routine dental practice, mainly due to 1) limited data availability, accessibility, structure, and comprehensiveness, 2) lacking methodological rigor and standards in their development, 3) and practical questions around the value and usefulness of these solutions, but also ethics and responsibility. Any AI application in dentistry should demonstrate tangible value by, for example, improving access to and quality of care, increasing efficiency and safety of services, empowering and enabling patients, supporting medical research, or increasing sustainability. Individual privacy, rights, and autonomy need to be put front and center; a shift from centralized to distributed/federated learning may address this while improving scalability and robustness. Lastly, trustworthiness into, and generalizability of, dental AI solutions need to be guaranteed; the implementation of continuous human oversight and standards grounded in evidence-based dentistry should be expected. Methods to visualize, interpret, and explain the logic behind AI solutions will contribute ("explainable AI"). Dental education will need to accompany the introduction of clinical AI solutions by fostering digital literacy in the future dental workforce.

Keywords: decision-making; deep learning; dental; diagnostic systems; informatics; machine learning.

Figures

Figure 1.

Natural and computer intelligence. Natural intelligence is characterized by perception, interpretation and biological response. In contrast, computer intelligence does so far not replace human responses, but largely supports human interpretation and action. Traditional software (1.0) as one pillar of computer intelligence is supported by rules-based expert systems; they take data and explicitly programmed logical rules to generate narrow, specialized outcomes, thereby outperforming humans in these tasks. Software 2.0 instead uses data and outcomes to infer the rules: In classical machine learning, the features are first engineered by human experts and then learned (e.g., regression modeling). In deep learning, relevant features are learned and mapped in one step, without human feature engineering; this allows to leverage even complex data structures like imagery or language. Modified after Kolossváry et al. (2019).

Figure 2.

Milestones in the development of artificial intelligence (AI). AI refers to machines that are capable of performing tasks that are normally performed by humans. Machine learning (ML) involves the representation (learning) of intrinsic statistical patterns and structures in data, which allows for predictions for unseen data. “Deep Learning” is a form of machine learning in which multi-layered (deep) neural networks (NNs) are trained to learn features of complex data structures (e.g., image data or language). The history of AI is characterized by ups and downs; after numerous setbacks, optimism is greater today than ever before.

Figure 3.

More transparency through explainable AI (XAI). (A) Today’s AI models are often considered black boxes, because they take an input (e.g., an image) and provide a prediction (e.g., “rooster”) without saying how and why they arrived at it. (B) Recent XAI methods (Samek et al. 2019) redistribute the output back to input space and explain the prediction in terms of a “heatmap,” visualizing which input variables (e.g., pixels) were decisive for the prediction. (C) This allows to distinguish between meaningful and safe prediction strategies, for example, classifying rooster images by detecting the roster’s comb and wattles or classifying cat images by focusing on the cat’s ears and nose, and so-called Clever Hans predictors (Lapuschkin et al. 2019), for example, classifying horse images based on the presence of a copyright tag.