Abstract
Introduction
The rapid expansion of digital technologies in dentistry has resulted in increased reliance on complex clinical and imaging data, creating opportunities for artificial intelligence (AI) to support diagnostic and treatment-related decision-making. While AI-based systems have demonstrated promising results, their clinical relevance and ethical implications require critical evaluation. This narrative review aims to evaluate the current evidence on AI applications in dentistry with particular emphasis on clinical utility, validation, and ethical considerations.
Methods
A narrative review of the literature was conducted using peer-reviewed studies focusing on AI applications across dental specialties, including dental imaging, orthodontics, endodontics, prosthodontics, implant dentistry, oral medicine, and oral pathology. Emphasis was placed on clinically relevant outcomes, validation approaches, and real-world applicability.
Results
AI has shown substantial potential as a decision support tool in dentistry, particularly in diagnostic imaging, automated analysis, and treatment planning. Applications such as caries detection, cephalometric landmark identification, periapical pathology detection, and digital smile design demonstrate improved efficiency and consistency. However, most AI systems remain limited by retrospective study designs, lack of external validation, population bias, and restricted interpretability.
Conclusion
AI represents a valuable adjunct in modern dental practice when used to support, rather than replace, clinical judgment. Responsible integration of AI requires robust validation, ethical governance, clinician education, and regulatory clarity to ensure patient safety and equitable care.
Introduction
Dentistry has undergone rapid transformation over the past two decades, driven by advances in digital technologies such as cone-beam computed tomography (CBCT), intraoral scanning, computer-aided design and manufacturing, and digital treatment planning systems. 1 While these innovations have improved diagnostic accuracy and treatment efficiency, they have also generated large volumes of complex clinical data that often exceed the limits of conventional human interpretation. 2 This increasing data burden has emphasized the need for intelligent systems capable of assisting clinicians in pattern recognition, reducing variability, and supporting clinical decision-making.
Artificial intelligence (AI), a branch of computer science focused on enabling machines to perform tasks traditionally requiring human intelligence, has emerged as a potential solution. 3 In healthcare, AI has demonstrated promising applications in medical imaging, disease prediction, and outcome assessment, particularly through machine learning (ML) and deep learning (DL) techniques. 4 Dentistry, being a visually intensive and data-rich discipline, is especially suitable for AI integration. 5
Early AI applications in dentistry were largely experimental, concentrating on automated image interpretation. Recent advances, however, have shifted focus toward clinically oriented applications, including caries detection, periodontal assessment, orthodontic treatment planning, and oral cancer screening.6–8 These systems are designed to function as decision support tools that enhance diagnostic consistency and efficiency.
Despite encouraging results, clinical translation remains inconsistent. Many AI models demonstrate high accuracy in controlled settings, yet concerns persist regarding external validity, population generalizability, and real-world performance. 9 Ethical issues related to data privacy, algorithmic bias, transparency, and medicolegal responsibility have also gained importance. 10 This narrative review evaluates current evidence on AI applications in dentistry with emphasis on clinical utility, limitations, and ethical considerations, and discusses future directions for responsible integration into dental practice.
AI: Concepts Relevant to Dentistry
AI refers to the ability of computer systems to perform tasks that typically require human intelligence, including learning from data, recognizing patterns, and generating predictions or recommendations. 11 In dentistry, AI is intended to augment, not replace, clinical judgment by processing large datasets and identifying features that may not be readily perceptible to the human eye.
ML, a subset of AI, enables algorithms to learn from data without explicit programming, improving performance as data volume increases. 12 In dentistry, ML has been applied to radiographic classification, outcome prediction, and diagnostic decision support. DL, a further subset of ML, employs artificial neural networks with multiple hidden layers to model complex, non-linear relationships within data. 13 Convolutional neural networks (CNNs), a DL architecture optimized for image analysis, are widely used in dental radiology and oral pathology. 14
Artificial neural networks are inspired by biological neural systems and consist of interconnected nodes that process data through weighted connections. 15 These networks can analyze radiographs, photographs, and three-dimensional scans to detect disease-related patterns with minimal reliance on manually engineered parameters. Dentistry is particularly well-suited for AI integration due to its reliance on visual data and standardized diagnostic records. Radiographs, intraoral scans, facial photographs, and digital models provide structured datasets suitable for consistent analysis. 16 However, AI performance depends heavily on data quality and variations in imaging protocols, populations, and clinical practices may influence reliability. Understanding these limitations is essential for responsible clinical integration. 17
Current Evidence of AI Applications in Dentistry
AI in Dental Imaging and Radiology
Dental imaging forms the foundation of diagnosis and treatment planning across dental specialties. Conventional interpretation of radiographs is subject to inter- and intra-observer variability, fatigue, and differences in clinical experience. 18 AI-based systems, particularly DL models, have been investigated to address these limitations by providing consistent and objective image analysis. One of the most extensively studied applications of AI in dental radiology is caries detection on bitewing and periapical radiographs. CNNs have demonstrated diagnostic accuracy comparable to and occasionally exceeding that of experienced clinicians,6, 7, 19 particularly in identifying early or subtle lesions that may be overlooked during routine assessment.
AI has also shown promise in evaluating periodontal bone loss. Automated algorithms capable of quantifying alveolar bone levels and classifying disease severity may help standardize periodontal diagnosis and support longitudinal monitoring. 20 In endodontic imaging, AI models have been applied to detect periapical radiolucency and root morphology variations, although image quality and anatomical complexity remain challenges.21, 22
With increased use of CBCT, AI applications have expanded to three-dimensional image analysis, including tooth segmentation and anatomical structure identification.23, 24 However, many systems rely on limited or homogeneous datasets, and variations in imaging protocols and patient populations can affect performance. 9 Consequently, AI should function as a diagnostic adjunct, with final clinical responsibility remaining with the clinician.
AI in Orthodontics
Orthodontics is one of the most active domains for AI applications due to its reliance on imaging, measurements, and predictive treatment planning. Conventional orthodontic diagnosis depends on manual landmark identification, growth assessment, and clinician experience, all of which are time-consuming and more prone to variability. 25 AI-based systems have been developed to improve efficiency, reproducibility, and objectivity in these processes.
A well-established application of AI in orthodontics is automated cephalometric landmark detection. DL models, particularly CNNs, have demonstrated accuracy comparable to experienced orthodontists while significantly reducing analysis time and inter-operator variability.26–28 These systems function as decision support tools rather than replacements for expert interpretation. AI has also been explored for growth and treatment outcome prediction. ML algorithms analyzing longitudinal data have shown potential in forecasting mandibular growth patterns and treatment response.29, 30 However, predictive reliability remains influenced by dataset size, population diversity, and follow-up duration.
With increasing adoption of clear aligner therapy and digital workflows, AI-driven planning tools assist in tooth movement staging, aligner sequencing, and outcome simulation. 31 Despite these advances, many studies rely on retrospective and homogeneous datasets, raising concerns about generalizability and external validation. 32 Consequently, AI in orthodontics should be regarded as an adjunct that supports clinical judgment and individualized treatment planning.
AI in Endodontics
Endodontics is well-suited for AI applications due to its reliance on radiographic interpretation, anatomical complexity, and outcome prediction. Conventional diagnosis and treatment planning depend heavily on clinician experience, particularly for identifying root canal morphology, determining working length, and detecting periapical pathology. These tasks are often complicated by anatomical variations and limitations in image quality, contributing to diagnostic inconsistency. 33
AI-based systems, particularly DL models, have been investigated for detecting periapical lesions on periapical radiographs and CBCT images. CNNs have demonstrated high sensitivity and specificity in identifying periapical radiolucency, performing comparably to experienced endodontists under controlled conditions.23, 24 Such systems may be useful as screening or second opinion tools in complex cases.
AI has also been explored for identifying root canal morphology, including detection of additional canals using CBCT datasets. 34 Accurate recognition of canal anatomy is essential for treatment success. Furthermore, ML models have been applied to predict endodontic treatment outcomes based on preoperative variables, although these applications remain investigational and require further validation. 35 Overall, AI in endodontics should be regarded as a supportive tool that enhances diagnostic confidence rather than a substitute for clinical expertise.
AI in Prosthodontics and Implant Dentistry
Prosthodontics and implant dentistry depend on accurate diagnosis, esthetic planning, and biomechanical precision. Digital workflows such as intraoral scanning, virtual smile design, and computer-guided implant placement have provided a strong foundation for AI-based applications. In this field, AI is primarily used for treatment planning, esthetic evaluation, and risk assessment to improve predictability and efficiency. 36
A prominent application of AI in prosthodontics is digital smile design. AI-driven systems analyze facial photographs, dental proportions, and esthetic parameters to propose smile designs aligned with facial symmetry and patient-specific characteristics. 37 These tools enhance communication among clinicians, technicians, and patients, although final aesthetic decisions remain clinician-dependent.
In implant dentistry, AI has been explored for automated implant planning and identification of anatomical structures using CBCT data. DL models can assist in recognizing vital structures and suggesting optimal implant positioning, 38 potentially improving planning efficiency and safety. AI has also been investigated for predicting implant success and marginal bone loss, though most models remain retrospective and lack long-term validation.39, 40 Overall, AI should be regarded as a supportive planning tool rather than an autonomous decision maker in prosthetic and implant care.
AI in Oral Medicine and Oral Pathology
Oral medicine and oral pathology focus on diagnosing mucosal diseases, potentially malignant disorders, and oral cancers, where early detection is critical for prognosis. Conventional diagnosis relies on clinical examination, adjunctive imaging, and histopathological interpretation, all of which are subject to observer variability and diagnostic delay. 41 AI has therefore been explored as a supportive tool to improve diagnostic accuracy and consistency.
One of the most studied applications of AI in oral medicine is the detection and classification of oral potentially malignant disorders and oral squamous cell carcinoma. DL models trained on clinical photographs and histopathological images have demonstrated promising accuracy in differentiating malignant from benign lesions. 42 These systems may assist in screening and triaging, particularly in high-risk or resource-limited settings, but do not replace biopsy or expert evaluation.
In oral pathology, AI-based analysis of digitized histopathological slides has shown potential in identifying tumor regions and classifying jaw lesions. 8 However, most studies remain retrospective with limited external validation across diverse populations. 43 Consequently, AI should be regarded as a decision support adjunct, with definitive diagnosis and clinical responsibility remaining with trained clinicians.
Clinical Utility of AI in Dentistry
Although much of the literature on AI in dentistry emphasizes algorithmic performance and diagnostic accuracy, its true value lies in clinical utility. AI systems are best regarded as decision support tools that assist clinicians in interpreting data, reducing variability, and improving efficiency rather than as autonomous decision makers. 2 This distinction is essential for responsible integration into routine dental practice.
A key clinical benefit of AI is the reduction of inter- and intra-operator variability. Tasks such as radiographic interpretation, landmark identification, and lesion detection are influenced by clinician experience and workload. AI-assisted analysis can provide consistent baseline assessments, helping clinicians verify findings and reduce oversight, particularly in high-volume clinical settings. 44
AI also enhances workflow efficiency. Automated image analysis, cephalometric tracing, and treatment simulations reduce time spent on repetitive tasks, allowing clinicians to focus on patient interaction and complex decision-making. In orthodontics and prosthodontics, AI-supported planning improves visualization of treatment outcomes and interdisciplinary communication. 45
Personalization of care represents another important advantage. By analyzing demographic, clinical, and imaging data, AI systems may support individualized treatment planning and outcome prediction, facilitating informed consent and shared decision-making. 10 However, AI outputs must always be interpreted within the broader clinical context, as algorithms cannot account for patient preferences or nuanced clinical judgment. Effective use therefore depends on clinician literacy and critical appraisal of AI-generated results. 27
Accuracy, Validation, and Generalizability of AI Systems
Accuracy is often the most emphasized outcome in AI research; however, high performance metrics alone do not ensure clinical reliability. In dentistry, many AI systems demonstrate strong sensitivity and specificity during development, yet real-world effectiveness depends on robust validation and generalizability across diverse clinical environments. 46
Validation of AI models typically occurs at three levels, internal, external, and clinical validation. Internal validation, performed using subsets of training data, frequently produces optimistic results. External validation, which assesses performance on independent datasets from different populations or institutions, is more critical for real-world applicability but remains limited in much of the dental AI literature. 47
Generalizability is a major concern. AI models trained on homogeneous datasets from single centers, specific imaging devices or narrow demographic groups may underperform when applied to different populations. Variations in imaging parameters, scanner types, disease prevalence, and ethnic craniofacial characteristics can significantly affect algorithm performance. 48 Insufficient dataset diversity may therefore reinforce systematic bias rather than improve diagnostic equity.
Algorithmic bias represents an additional challenge, as AI systems may unintentionally reflect imbalances in training data, reducing accuracy in underrepresented groups. 49 Furthermore, many AI models operate as “black boxes,” limiting interpretability and complicating clinician trust and medicolegal accountability. Explainable AI approaches are increasingly advocated to support responsible clinical adoption. 50
Future Perspectives
The future of AI in dentistry lies in its seamless integration with digital workflows, including intraoral scanners, facial analysis systems, and electronic health records. AI-driven tools may support more personalized diagnosis and treatment planning by combining clinical, imaging, and demographic data. Greater emphasis on external validation, explainable AI, and clinician training will be essential to ensure safe and equitable adoption. Rather than replacing clinicians, future AI systems are expected to function as collaborative tools that enhance precision, efficiency, and patient-centered care.46, 48
Conclusion
AI has demonstrated considerable potential across multiple dental specialties, particularly as a decision support aid. While current evidence supports its diagnostic and workflow benefits, challenges related to validation, ethics, and implementation remain. Responsible integration of AI, guided by clinical judgment and ethical oversight, is essential to ensure that technological advancement translates into meaningful improvements in dental care.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical Approval Institutional Statement and Informed Consent
This study is a literature review based exclusively on previously published data and does not involve human participants, animals, or identifiable personal data. Therefore, ethical approval and informed consent were not required.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
