Guest Editors:
Prabhat Kumar Chaudhari, MDS, MFDS RCPS (Glasg), MFDS RCS (Eng), MDTFEd, MAMS: All India Institute of Medical Sciences, India
Kunaal Dhingra, MDS, MFDS RCPS (Glasg), MFDS RCS (Eng), MDTFEd, MAMS: All India Institute of Medical Sciences, India
Raša Mladenović, DDS, PhD: University of Kragujevac, Serbia
Sergio Uribe, DDS, MSc, PhD: Riga Stradins University, Latvia/ Austral University of Chile, Chile
Prabhat Kumar Chaudhari, MDS, MFDS RCPS (Glasg), MFDS RCS (Eng), MDTFEd, MAMS: All India Institute of Medical Sciences, India
Kunaal Dhingra, MDS, MFDS RCPS (Glasg), MFDS RCS (Eng), MDTFEd, MAMS: All India Institute of Medical Sciences, India
Raša Mladenović, DDS, PhD: University of Kragujevac, Serbia
Sergio Uribe, DDS, MSc, PhD: Riga Stradins University, Latvia / Austral University of Chile, Chile
Artificial intelligence (AI) is the theory and development of computer systems that perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. The human brain's visual cortex architecture inspires Deep Learning (DL). Hence DL produces artificial neural networks that can learn by themselves. These networks can deal with minor nuances and become faster and more precise the more data they analyse. Even in the primitive stage of its development, AI, for the last two to three decades, has drawn the attention of brilliant minds from both technical and clinical corners of the scientific community, thus making rapid strides. In most developing countries, the insufficiency of medical and dental specialists has increased the mortality of patients suffering from various diseases. In medical and dental applications, employing technology, especially artificial intelligence technology, could reduce cost, time, human expertise, and medical error. This approach can potentially revolutionise the dental public health scenario in developing countries. Clinical decision support systems and dental monitoring are computer programs that provide expert support for health professionals. The applications of AI in dental sciences vary from dental emergencies to the differential diagnosis of orofacial pain, dental caries, and periodontal diseases, radiographic interpretations, and analysis of facial growth. However, despite the recognized need for artificial intelligence, implementing these systems has been minimal and slow. In this special Collection, we welcome papers related to the possible applications of AI in dental diagnosis and treatment. BMC Oral Health has published this Collection to bring together research on:
We strongly recommended that authors refer to the minimum reporting guidelines for health research hosted by the EQUATOR Network when preparing their manuscript, and FAIRsharing.org for reporting checklists for biological and biomedical research, where applicable. Those appropriate for AI studies are listed below:
Artificial intelligence in dental research: Checklist for authors, reviewers, readers
For clinical trial reports for interventions involving AI, independent of the AI system modality (diagnostic, prognostic, therapeutic), we recommended following the CONSORT-AI Extension reporting guidelines. CONSORT-AI focuses on effectiveness and safety.
For clinical trial protocols for interventions involving AI we recommended following the SPIRIT-AI Extension reporting guidelines.
For reporting the early-stage clinical evaluation of decision support systems driven by AI we recommended following DECIDE-AI guidelines. These guidelines are used to report the early evaluation of artificial intelligence systems as an intervention in live clinical settings (small-scale, formative evaluation), independently of the study design and AI system modality (diagnostic, prognostic, therapeutic). Focuses on clinical utility, safety and human factors.
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Image Credits: IoT World Today
Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditi...
Integrating artificial intelligence (AI) into medical and dental applications can be challenging due to clinicians’ distrust of computer predictions and the potential risks associated with erroneous outputs. W...
Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous det...
The purpose of this study is to explore the perspectives, familiarity, and readiness of dental faculty members regarding the integration and application of artificial intelligence (AI) in dentistry, with a foc...
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques.
Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on c...
The grading of oral epithelial dysplasia is often time-consuming for oral pathologists and the results are poorly reproducible between observers. In this study, we aimed to establish an objective, accurate and...
Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the ...
To predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar re...
Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This stud...
Artificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in...
The Correction to this article has been published in BMC Oral Health 2025 25:167
Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dent...
Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, cr...
Oral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reduci...
Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological me...
This study aimed to validate the availability of superimposing full-color mandibular digital models (DMs) by the morphological characteristics of vessels in extraction adult patients.
Considering the prevalence of Periodontitis, new tools to help improve its diagnostic workflow could be beneficial. Machine Learning (ML) models have already been used in dentistry to automate radiographic ana...
Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is pla...
Adequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study...
Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, ca...
Owing to the remarkable advancements of artificial intelligence (AI) applications, AI-based detection of dental caries is continuously improving. We evaluated the efficacy of the detection of dental caries wit...
Accurate cephalometric analysis plays a vital role in the diagnosis and subsequent surgical planning in orthognathic and orthodontics treatment. However, manual digitization of anatomical landmarks in computed...
Dental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This i...
The purpose of this study was to automatically classify the three-dimensional (3D) positional relationship between an impacted mandibular third molar (M3) and the inferior alveolar canal (MC) using a distance-...
The integration of artificial intelligence (AI) in dentistry has the potential to revolutionise the field of dental technologies. However, dental technicians’ views on the use of AI in dental technology are st...
Having a reliable and feasible method to estimate whether an individual has reached 16 years of age would greatly benefit forensic analysis. The study of age using dental information has matured recently. In a...
Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high...
We aimed to develop a 5-year overall survival prediction model for patients with oral tongue squamous cell carcinoma based on machine learning methods.
To evaluate the techniques used for the automatic digitization of cephalograms using artificial intelligence algorithms, highlighting the strengths and weaknesses of each one and reviewing the percentage of su...
To assess and compare the impact of various computers aided design/manufacturing (CAD/CAM) materials on internal and marginal discrepancies, fracture resistance and failure probability of Endocrown restoration...
The application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output ...
Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially...
One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed ort...
In this study, we investigated whether deep learning-based prediction of osseointegration of dental implants using plain radiography is possible.
Preoperative planning of orthognathic surgery is indispensable for achieving ideal surgical outcome regarding the occlusion and jaws' position. However, orthognathic surgery planning is sophisticated and highl...
It is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandi...
This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system, SNAPP. During the submission process you will be asked whether you are submitting to a Collection, please select "Artificial Intelligence: Applications in Dentistry" from the dropdown menu.
Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.
The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.