Turing’s famous test, introduced in the 1950s, raised many philosophical and technical debates. Around the same period, scientists started building simple computational models based on how the human brain might function. These early steps laid the groundwork for what we now know as artificial intelligence. Over the decades, the ideas grew. In the 1980s and 1990s, more structured forms of machine learning began appearing. And by the early 2000s, with faster processors and more data, AI started becoming a part of many scientific fields.(1) In recent years, AI has found its way into healthcare. This was not sudden. First came the use of computers in managing patient records, followed by digital imaging. Then, as algorithms became more advanced, AI began assisting doctors in analyzing X-rays, CT scans, and even microscopic slides. Radiology and pathology, in particular, saw huge changes. The accuracy of AI tools started matching that of trained specialists. And not only in hospitals, AI became useful in rural settings too, where medical expertise is less available. Dentistry also followed the trend.(2) In the field of dentistry, especially in periodontics, the interest in AI has been increasing. Periodontics, as is well known, focuses on the tissues supporting the teeth. These include the gums, alveolar bone, cementum, and periodontal ligament. Periodontal diseases are common—affecting millions around the world. Various studies mention that about 20 to 50 percent of adults may have some form of periodontal disease. Among them, severe periodontitis affects nearly 11% of the global population. This condition not only leads to tooth loss but also has links with other illnesses like diabetes and cardiovascular disorders.(3) Traditionally, diagnosing periodontal disease involved manual probing, examining pocket depths, looking at clinical attachment levels, and evaluating radiographs. While these tools are useful, they are not always consistent. Different clinicians may record different readings. And radiographs may not always show early bone loss. Early diagnosis often becomes difficult. Treatment planning, too, depends on accurate data. Follow-ups require comparisons with earlier records. However, these methods can be subjective and vary from one practitioner to another. That’s where AI might be helpful.(4) ROLE OF ARTIFICIAL INTELLIGENCE: AI, or artificial intelligence, includes many branches. Among them are machine learning (ML) and deep learning (DL). ML involves systems that learn from previous data. The more data they get, the better they become. DL, on the other hand, uses neural networks that mimic how the brain functions. These systems can identify patterns in large, complex datasets. In healthcare, this is especially valuable. Because in medicine, data comes in many forms—images, lab values, clinical notes, and more. AI can help find hidden patterns that humans might miss.(5) AI’s success in other branches of medicine gave rise to interest in its use in dentistry. In radiology, for instance, convolutional neural networks (CNNs) have been trained to identify tumors, fractures, and infections on X-rays. The results have been promising, with many studies showing AI performance comparable to senior radiologists. In pathology, AI tools can examine tissue samples and classify diseases in a matter of minutes. This reduces the time for diagnosis and lowers interobserver variations. Encouraged by these achievements, dental researchers started looking into how AI could be used in oral health.(6) In the dental field, AI tools are being used in many areas. Caries detection is one example. Orthodontics has also benefited, with AI being used for planning tooth movements and simulating treatment outcomes. Implant placement is becoming more precise with AI assistance. These developments have made dental procedures faster, more efficient, and in some cases, more affordable.(7) Now coming back to periodontics, this specialty has its own set of diagnostic challenges. The changes that occur in periodontitis, such as alveolar bone loss or deep periodontal pockets, may not be visible early. And bone loss, when it appears on radiographs, is already a sign of established disease. Here, AI can play a significant role. Using deep learning models, it is now possible to automatically detect alveolar bone changes in periapical radiographs or cone-beam computed tomography (CBCT) images. These tools can highlight areas of bone loss, track changes over time, and assist the periodontist in making quicker and more informed decisions.(8) Since then, AI has also been applied in predicting periodontal disease progression. Machine learning models can analyze data from clinical records, radiographs, microbiological findings, and even systemic health parameters to predict which patients are more likely to develop severe disease. These models are not only diagnostic but prognostic. They help in risk assessment and allow for personalized treatment plans.(9) Moreover, AI can monitor treatment outcomes. This means that after a scaling and root planing procedure or surgery, patient progress can be tracked using AI algorithms. The system compares current data with previous records and tells whether the disease is improving, stable, or worsening. This reduces guesswork and makes patient management more objective. Some AI tools have also been developed for teledentistry. These platforms use AI to evaluate photographs or radiographs uploaded by patients from remote locations. Especially during the COVID-19 pandemic, when physical consultations were limited, such tools proved valuable. Periodontal evaluations, although ideally performed in person, were possible to some extent using AI-supported teledentistry.(10) According to recent literature, AI models have achieved high sensitivity and specificity in identifying periodontal defects. Several studies have shown that AI can detect bone loss more accurately than traditional methods. Some commercial systems are already being introduced into clinical practice. These tools can integrate with existing dental software, providing real-time suggestions to the dentist during patient examination.(11) However, despite the advantages, there are some concerns too. AI models depend heavily on the quality of the data they are trained on. If the training dataset has biases—say, more cases from one age group or population—the model may not perform well in other settings. Data privacy is another issue. Patient records need to be handled carefully. There is also the need to ensure that AI tools are compatible with the various types of clinical software used across dental practices.(12) ETHICAL CONSIDERATIONS Ethical considerations have started coming up as well. For example, who is responsible if an AI tool makes an incorrect prediction? Can AI replace the role of a periodontist? Most experts believe that AI is not here to replace professionals but to support them. Still, regulations and guidelines are needed. At present, many dental associations are working on drafting frameworks for AI integration in clinical care.(13) Thus, the field of periodontics is undergoing a shift. With AI entering the picture, the way periodontal diseases are diagnosed, treated, and followed up is slowly changing. What began as a theoretical idea has now moved into practical use. And as technology continues to evolve, its role in periodontal care is only expected to grow.(14) Accordingly, this Library Dissertation aims to critically examine the current applications of artificial intelligence in periodontics. The objective is to explore how these technologies are being used, what benefits they offer, what challenges remain, and how future research can guide their better integration into routine practice. With accurate diagnosis, individualized care, and improved patient outcomes being the goal, AI could become an essential part of periodontal treatment in the years to come. CLASSIFICATION OF PERIODONTAL DISEASES The classification system in periodontics has undergone notable changes, particularly in the last decade. The 2017 World Workshop—conducted in collaboration between AAP and EFP—introduced a much more refined classification model. One of the major changes was dropping the older terms "chronic" and "aggressive" periodontitis, which were often overlapping or confusing. Now, all are diagnosed under the unified term "periodontitis," and further subdivided using staging and grading systems.(18) Staging helps determine how severe the disease is, how much attachment and bone is lost, and how complicated the management might be. There are four stages, from Stage I (initial) to Stage IV (severe disease with functional compromise). Grading, on the other hand, gives an idea about how fast the disease is progressing and what kind of treatment response is expected—Grade A being slow, and Grade C being rapid progression. Risk factors like tobacco use and poor glycemic control in diabetics also influence grading.(19) Apart from periodontitis, gingival diseases are classified into plaque-induced and non-plaque-induced types. • Plaque-induced gingivitis: Though common, it is reversible and can usually be managed with routine oral hygiene. Hormonal changes (like during pregnancy), systemic diseases, or even certain drugs can modify its course. • Non-plaque-induced gingivitis: Lesions are less common but include serious conditions like necrotizing periodontal diseases, hereditary gingival fibromatosis, or immune-mediated disorders like lichen planus. These may be infectious (bacterial, fungal, viral) or idiopathic. The revised classification helps clinicians arrive at more precise diagnoses, improve communication with patients, and plan treatments that are both evidence-based and individualized.(20) AI PLATFORMS AND SOFTWARE IN DENTISTRY: A number of advanced AI platforms are now integrated into dental diagnostics, changing how clinicians interpret imaging and manage patient data. • IBM Watson, though more commonly used in general medical applications, is capable of adapting to dental analytics through cognitive computing. • Diagnocat is a cloud-based AI platform tailored for dentistry. It processes panoramic, CBCT, and intraoral images to identify findings like periapical lesions, retained roots, and bone loss (Figure 1). • Overjet, which is FDA-cleared, uses deep learning models to detect and measure caries, calculus, and bone levels, assisting in periodontal monitoring. • Dentem offers a combination of AI diagnostics and practice management tools, providing support for both imaging analysis and patient workflow. These platforms mark a significant transition toward precision dentistry. They help reduce diagnostic error, automate time-consuming tasks, and enhance clinical decision-making by combining speed with consistency.(15,16).

CURRENT APPLICATIONS OF AI IN PERIODONTICS Diagnostic Applications The landscape of periodontal diagnosis has begun shifting. Especially with artificial intelligence (AI) making strong inroads. Traditionally, periodontal probing—though foundational—often suffered from issues like examiner variability, subjective bias, and limited reproducibility. Now, with AI-assisted probing systems, including sensor-equipped probes backed by machine learning algorithms, measurements of periodontal pocket depths are becoming more consistent. These digital systems offer real-time data processing, which helps in reducing manual error and allowing quicker chairside assessments. Sometimes the results are even more reliable than those by manual charting done by experienced clinicians. More recent developments have focused heavily on radiographic assessment. Convolutional neural networks (CNNs), a type of deep learning architecture, are now trained to detect alveolar bone loss patterns across large datasets of periapical and panoramic radiographs. This tech doesn't just pick up on advanced bone defects—it flags early, subtle signs too. Alveolar crest blunting, interdental bone changes, or mild vertical defects that might otherwise be overlooked in a busy practice—AI catches them. This allows for earlier interventions and better long-term periodontal prognosis. Also, these tools remove inter-observer bias, which has always been an issue in radiographic interpretation. In risk profiling too, AI models have shown good reliability. They combine clinical parameters like gingival index, plaque score, probing depth, bleeding on probing—along with lifestyle data such as smoking, age, diabetes status, and even genetic polymorphisms. All these go into machine learning models like support vector machines (SVMs) and random forest classifiers. These models estimate an individual’s risk of developing gingivitis or progressing to periodontitis. The clinician gets a better understanding of who needs preventive care urgently—and who may be monitored with less frequent visits. This is real-time risk modeling; very helpful for tailoring care.(16) CBCT-based AI models are especially helpful in detecting calculus, furcation involvement, and other subgingival findings. In clinical setups, detecting these structures can be difficult due to anatomical variations or radiographic limitations. But AI-based segmentation and detection algorithms can isolate mineralized deposits, furcal defects, or intrabony lesions with precision. It minimizes the chances of missing important diagnostic findings. What’s more, these tools can now be fused with periapical radiographs—merging multiple datasets—to provide a three-dimensional and comprehensive diagnostic picture. So the clinician can get depth, density, and spatial orientation in a single glance. This integration supports improved diagnostic clarity, especially in molars and complex cases.(17). Predictive Applications AI isn’t just for diagnosis. It's now finding value in predicting how diseases will behave over time. Recurrence prediction models have emerged using neural networks and time-series data. These systems analyze variables like previous attachment loss, pocket reformation rates, microbiological flora, immune markers, and systemic health status. The model then estimates how likely a patient is to experience recurrence of periodontitis post-treatment. With this insight, clinicians can individualize recall intervals and supportive periodontal therapy frequency. Prognostic stratification is another key application. Using AI, especially ensemble learning models, the likelihood of tooth loss or the success of a regenerative procedure can be modeled. These tools evaluate a large set of parameters: mobility grading, bone defect morphology, furcation class, root trunk length, and even patient compliance history. Based on this, teeth are categorized into different risk zones—low, moderate, or high. That helps the periodontist decide whether a tooth should be retained, monitored, or extracted. These stratification tools support evidence-based decisions, and they bring consistency in cases where traditional clinical judgment can be variable. In both diagnostics and prognosis, AI brings structure and objectivity. But it still needs good clinical data. And interpretation always needs a trained human. The technology doesn't replace the clinician—but it supports them.(18). Therapeutic Applications Artificial intelligence has found a significant role in therapeutic applications within periodontics. Especially in streamlining and improving treatment strategies, its presence is growing fast. One of the major transformations it brings is in treatment planning. Instead of a generalized approach, AI systems—particularly those based on machine learning and neural network algorithms—now assist in tailoring treatment plans to each patient's individual profile. This includes clinical data, radiographs, genetic predispositions, lifestyle factors and systemic comorbidities. All these together help in arriving at a more patient-centric decision. Treatment strategies designed through such AI-assisted models often result in enhanced outcomes and reduced variability. Also, it lowers dependency on subjective clinical judgments. These tend to vary between clinicians, naturally. In regenerative periodontal therapy, AI helps clinicians in several ways. It includes analysis of morphometric bone loss, detection of vertical defects and selection of cases for regenerative procedures like guided tissue regeneration or guided bone regeneration. Earlier, these decisions were mostly intuitive. Or they were made based on the clinician’s experience alone. Now, with AI’s help—especially using convolutional neural networks and diagnostic imaging data—clinicians are able to predict regenerative outcomes with better confidence. It gives better foresight. Also, AI-guided software can suggest selection of appropriate biomaterials—like alloplasts, xenografts, or resorbable membranes—depending on defect characteristics. This improves healing potential. Sometimes, AI models may even predict where regeneration won’t work. That helps in avoiding unnecessary treatment.(19) Robotic-assisted surgeries have slowly begun making their way into periodontics. Though still in early phases compared to other surgical specialties, AI-backed robotics are being applied in precision tasks. These include implant placement, osseous recontouring and microsurgical periodontal procedures. The accuracy offered by robotic arms—when guided by 3D imaging data and real-time feedback—has shown promising results. They reduce hand tremor. They limit tissue trauma. And overall, they support more predictable recovery after surgery. Some of these robotic systems are semi-autonomous. That is, the surgeon still controls them, but with added precision and motion guidance. Clinical trials in this field remain limited, but early outcomes are encouraging. Navigation tools for flap surgeries are also seeing improvements through AI integration. These systems now use intraoperative imaging and AI-based anatomical recognition. So, surgeons can execute incisions and debridement with greater control. With augmented reality overlays, even the depth and extent of bone loss becomes visible during surgery. These tools, besides enhancing precision, help reduce the chances of surgical error. That’s particularly useful for less experienced surgeons. Or in academic settings, where anatomical preservation is critical. In some cases, even the angle of the blade or the dissection path may get adjusted based on AI-based predictive overlays. This kind of intraoperative guidance might become standard in teaching centers over the coming decade.(20). Monitoring and Follow-up Monitoring of periodontal health, traditionally dependent on manual probing and clinical visits, is now being reshaped by AI-based wearable technologies and mobile applications. Smart toothbrushes, for instance, embedded with AI algorithms, can track brushing patterns. They record the force applied, plaque detection, and connect to apps that give feedback to the user in real time. Some even send this data to the dentist. That way, early signs of gingival inflammation or poor hygiene can be flagged before clinical symptoms become visible. A few platforms also integrate salivary biosensors. These detect markers like interleukin-1β or matrix metalloproteinase-8. When these rise, it suggests early periodontal breakdown. These are promising developments, though still needing broader clinical validation. Teleperiodontics is another area growing steadily (Figure 2). It is especially valuable for post-surgical monitoring or in rural regions with poor specialist access. AI enhances these systems by analyzing radiographs, intraoral photographs and even uploaded periodontal charting before the periodontist sees them. So, delays in diagnosis get reduced. Patients can share data asynchronously. AI processes the inputs and flags abnormal patterns. This cuts down on unnecessary in-person visits and makes specialist care more accessible. In public health clinics or outreach programs, this is proving cost-effective and scalable.(21)

AI-DRIVEN IMAGING AND DATA INTERPRETATION IN PERIODONTICS A) Analysis of 2D and 3D Radiographs B) Deep Learning Models in Radiographic Diagnostics C) Morphometric Assessments of Bone Loss D) Photographic and Colorimetric Analysis E) Dental Informatics and EHR Integration CLINICAL DECISION SUPPORT SYSTEMS (CDSS) IN PERIODONTICS A) CDSS Architecture for Periodontal Use B) AI Algorithms for Treatment Tree Decisions C) Personalized Periodontal Therapies D) AI-augmented Evidence-Based Practice AI IN PERIODONTAL RESEARCH AND PUBLIC HEALTH A) Data Mining for Risk Factors B) Predictive Models in Epidemiology C) Longitudinal/Cross-sectional AI-Based Studies D) AI in Meta-analyses and Systematic Reviews COMPARATIVE EVALUATION: AI VS TRADITIONAL PERIODONTICS A) Accuracy and Sensitivity B) Efficiency and Cost Comparison C) Reproducibility and Operator Bias D) Standardization and Error Minimization E) Patient Satisfaction and Clinical Outcomes CHALLENGES AND LIMITATIONS OF AI IN PERIODONTICS A) Data Collection and Labeling Issues B) Model Generalizability and Overfitting C) Lack of Validation in Clinical Settings D) Integration Challenges with Dental Practice Software E) Imaging Quality and Dependency ETHICAL, LEGAL, AND REGULATORY CONSIDERATIONS A) Patient Consent and Data Privacy (HIPAA, GDPR) B) AI Bias and Fairness C) Accountability in AI-Aided Decision Making D) Regulatory Frameworks (FDA, CDSCO, DCI) E) Ethical Use in Diagnostic vs Therapeutic AI FUTURE OF AI IN PERIODONTICS A) Autonomous AI Tools for Periodontal Diagnosis B) Robotics and Nanotechnology Integration C) Precision Periodontics through Genomics and Proteomics D) Cross-disciplinary Integration (Perio-Systemic Links) E) AI in Education: Virtual Reality and Simulations

Artificial Intelligence is changing periodontics in many ways. It helps in diagnosing diseases, planning treatments, and predicting outcomes. AI tools can analyze images quickly and sometimes better than humans, especially when it comes to spotting bone loss or other subtle changes, which is very useful. This can lead to earlier detection and better care. Besides diagnosis, AI also helps to assess risk for individual patients. This allows dentists to make decisions tailored to each person, which is a big step forward from one-size-fits-all treatment approaches. However, there are still some problems. The quality of the data AI learns from can vary a lot. Sometimes, the datasets are small or biased, which can affect how well AI works in real life — this is a concern. Also, there are privacy concerns with handling patient information. Not to forget, AI tools need thorough testing on different populations before they can be fully trusted. Periodontists, computer experts, and other specialists must work together to improve these systems and make sure they fit well into daily practice. Looking to the future, AI might do even more, like monitoring patients continuously through wearable tech or linking directly to health records for better decision making; however, it should be used as an aid, not a replacement for clinical judgment. Overall, AI shows great promise, but cautious and careful steps are needed to bring its full benefits to periodontics. With ongoing research and collaboration, it can truly enhance periodontal health care around the world.

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