Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. It has transformed numerous industries through newfound efficiencies and supportive decision-making. In dentistry, AI can benefit clinicians with high-quality patient-care, and simplifies complicated protocols by providing a predictable outcome, visualized anatomic guidance for treatment, as well as the occurrence and prognosis of oral diseases. 


Application of AI in dentistry 

AI has been used in dentistry in different ways and in various situations. AI models have shown the potential for providing a reliable diagnostic tool for tooth shade selection, restoration design, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Hereby are some of the most relevant applications of AI in dentistry:

  • Simple diagnosis

AI can be used to diagnose dental caries, with the accuracy of 89% in identifying dental caries in premolars, 88% in identifying dental caries in molars, and 82% in identifying dental caries between premolars and molars. AI assists clinicians by automatically generating treatment proposals based on deep learning algorithms. These treatment plans can potentially be tailored to clinicians’ treatment philosophies and desired outcomes. However, the treatment plans are still limited to simple restorative procedures such as fillings, partial coverage, and full coverage restorations.

  • Dental radiology interpretation

AI has been used to improve image interpretation in dental radiology. In 2D radiographs, such as digital radiographs, AI programs analyze the digital image to detect caries using pixel characteristics. In a recent paper, the potential applications of AI to identify interproximal caries using a series of bitewing radiographs was demonstrated.

In 3D radiographs, such as tomography scans, the annotation of anatomical landmarks and segmentation of bone and teeth structures can be achieved by AI. For instance,  Orca-Dental AI introduced their software with annotation abilities, automatically segmenting anatomical structures such as the maxilla, mandible, and teeth.

  • Dental implants

AI models were also proven useful for dental implants and prosthetics. Studies have shown that AI models developed to recognize implant type obtained an overall accuracy outcome of 93.8% to 98%; AI models to predict osseointegration success or implant success obtained an overall accuracy ranging from 62.4% to 80.5%; and AI models to optimize implant designs obtained an overall accuracy outcome of  36.6% for optimizing the implant design porosity, length, and diameter.

  • Smile design

Smile design has been a useful tool for team communication and patient motivation. Currently, there are around 15 smile design AI software to design the shape and alignment of the future smile. For instance, a new interactive cloud-based platform, Smilecloud, was introduced for incorporating digital smile design, the treatment plan, and the communication tool among clinicians, technicians, and patients. Once the required patient data (photos) are uploaded, the AI engine searches and proposes natural shapes of teeth and alignment. The proposed smile design can be versatilely modified by the clinician.

  • Orthodontics

In orthodontics, decision-making could be assisted with AI. The need for orthodontic treatment conventionally is based on comprehensive exams and patient analysis, as a result, the AI models had a success rate of 93% in differentiating extraction cases and non-extraction cases.  In a recent paper, it was revealed that AI could detect the orthodontic outcomes with an increase in facial attractiveness and a decrease in the appearance of age. In addition, machine learning was recently applied in orthodontic treatment simulation for prospective patients to pre-visualize the outcome of the treatment.

  • Oral diseases

Managing oral diseases has always been challenging due to the multifactorial nature of oral diseases, such as oral cancer or medication-related osteonecrosis of the jaw (MRONJ). In a pilot case report, different AI models were used to predict the occurrence of MRONJ.  This study concluded that using a machine learning model could achieve superior performance in prediction when compared to traditional assessments tools and techniques.

With the current trend and recent rapid development of AI, we can expect to see its impact on dentistry in the very near future. The progressive development of AI in dentistry will benefit clinicians and researchers to integrate different fields of knowledge and improve patient care. However, it is essential to be aware of the potential errors in interpretation of data via AI programs.


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