Artificial intelligence (AI) is a branch of computer science that involves simulating human intelligence in machines, particularly computer systems, to perform cognitive functions, such as learning, reasoning and self-correction with increased speed and accuracy (1). While standard software follows manually pre-defined processing steps to reach a result, AI tools use mathematical algorithms to independently identify patterns and leverage connections between inputs and desired outputs (2). John McCarthy coined the term ‘artificial intelligence’ around 1955 to refer to the ability of machines to perform tasks associated with intelligent behaviour (3), and in 1950, Alan Turing formulated the Turing test to evaluate whether a machine could demonstrate intelligence comparable to that of a human (4).

In the field of dentistry, particularly periodontology, AI and machine learning (ML) models are driving a marked transformation by integrating large volumes of clinical, radiographic and molecular data. AI enhances the accuracy, efficiency and consistency of diagnosis, treatment planning and patient management (1). Periodontal diseases, which range from gingivitis to periodontitis, are widespread and pose a substantial global health burden, affecting a large segment of the population and contributing to oral health issues worldwide (5). Accurate identification and diagnosis are challenging for clinicians owing to subjectivity, time constraints and inconsistencies in radiographic angulation and probing pressure inherent to conventional diagnostic methods in periodontology (6). AI provides a feasible option with which to overcome these limitations by potentially improving patient outcomes, treatment planning and diagnostic accuracy. The use of AI techniques in periodontal applications has exhibited a notable increase in publications since 2019, indicating that the field is gaining interest and has the potential to be revolutionised by AI (4,7).

Artificial neural networks (ANNs) and convolutional neural networks (CNNs), which form the foundation of medical image analysis in periodontology and can extract features from high-dimensional imaging data, are frequently employed in AI models (8,9). A range of data types are utilised as input, including genetic information, salivary biomarkers, clinical records, panoramic, periapical, bitewing, intra-oral and fluorescent images (7,9,10).

However, despite encouraging progress, the integration of AI into periodontology remains incomplete and faces several significant translational barriers (4,7). A key limitation is the insufficient understanding of AI principles among clinicians, combined with limited awareness of its practical applications and ongoing concerns regarding the reliability of AI-derived outputs (11). At the evidence level, marked heterogeneity in study designs, analytical methods and reported outcomes restricts interstudy comparability and limits the potential for robust evidence synthesis (2). Moreover, unresolved issues related to data quality, lack of standardised protocols, limited applicability across diverse populations and ethical concerns, including data privacy and security, continue to impede broader clinical implementation (6).

The application of AI in clinical settings could herald a new era of precision medicine for periodontal care in which treatment and diagnostic plans are customised to the individual needs of each patient (1,12). To provide a comprehensive perspective for both clinicians and researchers, the present narrative review aimed to summarise recent advances in AI-driven periodontal diagnostics and therapeutics, discuss the available data and explore potential future pathways in AI-based periodontology.

A comprehensive literature search was conducted for studies published between 2020 and 2025 using PubMed, Scopus, Web of Science and Google Scholar. The search was limited to articles in the English language. The search items included combinations of AI-related keywords (‘artificial intelligence’, ‘machine learning’, ‘deep learning’ and ‘neural networks’) and periodontal terms (‘periodontitis’, ‘periodontal disease’ and ‘dental diagnostics’) with Boolean operators (AND/OR). Reference lists of included articles were also hand-searched for additional relevant studies.

By providing advanced tools to enhance the diagnosis, monitoring and general management of periodontal diseases, AI is transforming healthcare, including the specialised field of periodontology (13). Conventional periodontal diagnosis relies predominantly on manual assessments, including clinical examination, periodontal probing to measure pocket depth and visual interpretation of radiographs. These methods are inherently subjective and prone to significant inter- and intra-operator variability, influenced by factors, such as probing force, radiographic angulation and clinician experience (12). AI provides a promising strategy with which to overcome these limitations by improving diagnostic accuracy, treatment planning and patient outcomes (6).

AI assists in devising a treatment plan by enabling the development of personalised strategies that support clinical decision-making (17).

AI is increasingly integrated into oral health devices and applications. Smart toothbrushes utilise sensors and algorithms to guide brushing. For example, Li et al (25) conducted a randomised trial comparing conventional care with an AI-enabled multimodal-sensing toothbrush (AI-MST) combined with personalised application feedback as an adjunct to standard periodontal therapy. After 6 months, the AI-MST group exhibited significantly improved outcomes: Only 44.4% of periodontal pockets remained inflamed compared with 52.3% in the control group. Furthermore, the test group achieved superior oral hygiene scores (25). This finding illustrates that AI-driven oral hygiene coaching can boost patient adherence and improve treatment efficacy. AI toothbrushes can advise patients on brushing pressure and timing, offering real-time feedback. Micro-robotic flossers and AI toothbrushes can automatically target biofilm in hard-to-reach areas. Clinicians may particularly recommend these for patients with poor dexterity or compliance (26). Mobile health applications complement these gadgets; AI-powered applications and wearables monitor brushing frequency, technique and other habits, providing personalised reminders and tips (6).

AI provides a revolutionary and objective approach to halitosis detection and monitoring, notably via the development of AI-powered electronic nose systems (27). AI systems for halitosis detection are based on artificial olfaction, also known as ‘electronic noses’. These systems combine principles of mammalian olfaction with AI to identify specific smell patterns. These systems use an array of non-selective sensors to assess the entire spectrum of exhaled volatile compounds (4,27). Nanomaterial-based sensor arrays coupled with ML algorithms have demonstrated remarkable capabilities. For example, systems comprising 32 metal-oxide sensors integrated with ML algorithms have achieved 98.1% accuracy in distinguishing various biological conditions by profiling volatile organic compounds (VOCs) (27,28). Deep neural networks (such as multi-layer perceptrons, CNNs and long short-term memory networks) are employed to interpret complex VOC patterns, capturing full VOC signatures rather than just volatile sulphur compounds, thereby enabling more accurate diagnosis and monitoring. To facilitate explainable analysis, the platform ‘OdoriFy’ uses a deep neural network to identify individual odorant molecules as malodorous and to predict their interactions with olfactory receptors (27).

AI is being used to analyse salivary and gingival crevicular fluid biomarkers to detect and treat periodontal diseases (29). These biological fluids are abundant and non-invasive sources of molecular indicators that reflect the overall state of periodontal and systemic health. Large datasets are generated from these fluids using ‘omics’ technologies (metabolome, microbiome, proteome, etc.), which are ideally suited for AI analysis to enhance our understanding of the pathophysiology of periodontal disease (1,30).

ML models trained on salivary counts of nine key periodontal bacteria have achieved 93% accuracy in distinguishing healthy from moderate or severe periodontitis, indicating that specific combinations of bacteria in saliva are effective biomarkers for disease severity (29). Support vector machine analysis using subgingival microbial profiles successfully classified periodontal status and distinguished between aggressive and chronic periodontitis with high accuracy (14). An ANN utilised immunologic parameters (e.g., leucocyte counts, interleukins and IgG antibody titres) to differentiate aggressive from chronic periodontitis with 90-98% accuracy (3).

AI has introduced autonomous robotic systems capable of performing dental procedures with sub-millimetre precision. The Yomi™ robot, for instance, is an FDA-approved robotic platform for dental implant surgery that employs AI algorithms for detailed surgical planning and provides real-time feedback to the surgeon (24,30,31). AI-driven robotics facilitates minimally invasive surgical techniques, which can minimise surgical trauma and promote rapid healing (30). For comprehensive treatment planning and precise surgical execution, these systems integrate with digital dental workflows, combining data from intra-oral scanners, CBCT and AI analysis. Real-time tissue characterisation and surgical planning adjustments are enabled by the synergy of AI and advanced imaging technologies. With this capability, surgeons can adapt their techniques to the individual anatomy and tissue properties of each patient.

Surgeons can visualise and rehearse procedures beforehand using surgical planning environments enabled by the integration of AI and mixed reality technologies. This approach reduces the learning curve for complex periodontal procedures while significantly enhancing surgical accuracy (32). A detailed summary of various AI tools, their specific applications and reported performance outcomes in periodontal diagnostics and therapeutics is provided in Table I.

Future research and development in AI for periodontology is warranted to focus on enhancing explainability and clinical relevance. Creating explainable AI systems that provide transparent and interpretable reasoning for their predictions is vital to building trust among clinicians and patients (35). Moreover, further research is required to highlight multimodal data fusion, combining clinical, imaging and molecular datasets, such as genomic and salivary biomarkers, in order to achieve a more precise, comprehensive understanding of periodontal disease dynamics (1,35). AI models need to advance to predict clinically significant outcomes, including disease severity, progression and personalised treatment responses, aligning with standardised diagnostic and therapeutic frameworks (14,39). The main limitations and the corresponding research directions to address them are outlined in Table II.

Equally critical is the need for longitudinal and prospective validation studies to evaluate AI model performance in predicting real-world disease trajectories and treatment outcomes, moving beyond the limitations of retrospective designs (6,9,14). Seamless clinical integration should also be a key focus. AI tools need to be interoperable, user-friendly and adaptable to existing dental workflows, with the potential for real-time disease monitoring via smart or home-based devices (8,14). Finally, rigorous comparative studies between AI and human clinicians using standardised datasets are essential to define the true capabilities and limits of AI in periodontal diagnosis and management (14).

AI is transforming periodontology by improving diagnostics, treatment planning and patient management. Tools such as CNNs, ANNs and NLP enable the precise detection of periodontal diseases, bone loss, gingival inflammation and complex defects from radiographs, intra-oral images, biomarkers and clinical data. Furthermore, AI supports personalised treatment protocols, the optimisation of non-surgical therapies, surgical or implant planning, smart oral health devices, halitosis detection, biomarker analysis and robotic-assisted procedures. In addition, it aids forensic applications such as age and sex determination and automated dental record matching.

However, in order to fully realise this potential, several challenges need to be addressed, including inconsistent data quality, lack of standardised protocols, limited generalisability and ethical concerns, such as privacy and patient trust. Large-scale, demographically diverse validation studies, interpretable AI models, federated learning for secure collaboration and robust cost-effectiveness research are necessary. Integration into clinical workflows requires investment in infrastructure, practitioner training and effective patient communication, while bridging the digital divide remains essential. Future progress depends on collaboration among clinicians, researchers, developers and regulators to ensure safe, ethical and equitable adoption. As AI merges with augmented reality/virtual reality, advanced sensors and mobile platforms, it promises to expand access and precision in periodontal care, ultimately improving global oral health outcomes.