Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review

Abstract

Objective. The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry. Materials and Methods. Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted. Results. The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics. Conclusion. The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

“…ML techniques can be also applied to other health-related fields that record patients' information as large amounts of unstructured texts, such as medicine (65). Integrating and using ML techniques to facilitate patient chart review will save time, reduce human error, provide a communication medium to use with patients and other providers, and enable practitioners and patients to locate and act upon the factors that play a major role in determining oral and general health status (7).…”

“…Radiographic topics included: (1) recession, (2) structurally compromised teeth, core, crown, (3) composite, tooth chip, pit, (4) lingual, wire, anterior, (5) exo, wisdom teeth, (6) wear, occlusal, (7) fracture, filling, (8) root canal therapy, amalgam, (9) decay, recurrent, (10) bone. Suggested treatment planning topics included: (1) tooth wear and invisalign, (2) possible replacing the composite filling, (3) root canal therapy and mesio-occluso-distal (MOD) filling, (4) tooth fracture and amalgam filling, (5) impression, (6) post, core and crown, (7) wisdom teeth extraction and possible orthodontic treatment, (8) oral hygiene and possible sedation. Common topics with their five most frequent terms are presented in Figure 3.…”

“…AI refers to computer algorithms that simulate the process of human intelligence to solve problems (6). Machine learning (ML) is a subset of AI techniques, which enable computers to learn from and identify relationships within data in different ways based on the specific algorithm used (7). For example, supervised algorithms are trained to learn using predictive models based on labeled data (i.e., data that humans give meaning to and describe by using preexisting understanding and knowledge) (8).…”

“…to new, unlabeled datasets to identify similar information. In contrast, unsupervised algorithms aim to discover unknown patterns in unlabeled data (i.e., data with no assigned meaning) and do not require human intervention for learning as these algorithms are tasked to identify key information on their own (7,(9)(10)(11). Medical and dental clinicians can utilize ML algorithms to manage and analyze the immense data present in patients' health records to extract meaningful information that can be used to improve population health and wellbeing (12).…”

Augmented, Virtual and Mixed Reality in Dentistry: A Narrative Review on the Existing Platforms and Future Challenges

“…The most active areas of medical AI are diagnostics and prediction of prognoses. AI in the medical sector contributes significantly to helping decision making related to medical practice, while presenting a considerable level of potential for sound diagnosis and prediction [61,62]. It was proven that machine learning, based on data derived from the decisions of dental professionals, achieved significant performance [63].…”