The article deals with the utilization of artificial intelligence (AI) and related technologies for the diagnosis of oral and maxillofacial anatomic variants and pathologic entities. The diagnosis of dental caries and periodontal disease is the most common aspect of dental diagnosis in a general dental setting and so, there was a subsection for this topic. There are dedicated portions within this article on applications of AI for oral and maxillofacial surgery.
Key points
-
•
Artificial intelligence (AI) can be engaged in the identification of normal radiographic anatomy, or abnormal anatomy; detection of lesions and the likelihood of the diagnosis; and detection of abnormalities in the maxillofacial region where practitioners may not completely grasp the anatomic features.
-
•
For the diagnosis of common dental conditions like caries and periodontal disease, AI-assisted analysis increases early detection, cuts down observer variability, and supports precise and personalized treatment planning.
-
•
The improvement in computing power, exponential production, and data collection has led to the rapid development of AI-based tools reducing surgical errors, limiting cognitive bias, and supporting surgical reasoning.
-
•
In radiology, the eventual goal would be to have a smooth transition to AI-powered and automated diagnostic radiology workflow to enhance patient care and safety. AI-supported interpretation of radiographic findings within the 2D or 3D image formats will bolster patient care with the goal of reducing medical errors.
-
•
The impact of AI on public health is undeniable, and its function in the field of teledentistry is rapidly developing into a highly sought-after patient care model.
Abbreviations
| AI | artificial intelligence |
| CNN | convolutional neural network |
| EHR | electronic health record |
| ML | machine learning |
| NLP | natural language processing |
| PACS | picture archiving and communications systems |
The role of artificial intelligence in diagnostic imaging
Interpreting imaging of the hard and soft tissue structures of the orofacial region is a highly specialized and complex task requiring detailed knowledge of anatomy, physiology, and pathology. Interpretation also calls for detailed pattern recognition that can lend itself well to the attributes of artificial intelligence (AI). Since many of the identifying characteristics of both normal and pathologic structures recorded in imaging studies can be quantified in specific ranges of radiographic density and distribution patterns and subjected to deep-learning algorithms, newly obtained images can be compared with the existing reference imaging datasets, yielding data on normal anatomy and providing descriptions and differential diagnoses of abnormalities and pathology. Areas of oral and maxillofacial diagnostic imaging ,,,,, where AI can be engaged include identification of normal radiographic anatomy, including orthodontic landmarks; identification of missing, variable, or abnormal anatomy; detection of lesions (both malignant and benign), differential diagnosis of lesions, along with the likelihood of the diagnosis; and detection of abnormalities in the temporomandibular joints, cervical spine, skull base, and other areas of the head and neck where practitioners may not completely grasp the anatomic features. Improvement in image quality, potentially leading to a definitive diagnosis derived from initial imaging studies is stressed rather than relying on further advanced imaging studies. This can potentially minimize the need for additional exposure and enhanced interpretation.
Many current projects aimed at providing software products available to dentists are still in the testing phase. AI products currently available to practicing dentists include programs for caries detection and periodontal bone level measurement. It should be noted that while AI applications for 2 dimensional imaging are already on the market, three-dimensional anatomy, as imaged with cone beam computed tomography (CBCT), is expected to be more challenging and will require additional development and testing.
Goals of AI in oral and maxillofacial imaging include improved performance of radiologists, time savings, seamless integration into the workflow, and minimizing costs.
Ethical concerns associated with using AI in oral and maxillofacial radiology include assurance to patients that the dentist retains the full responsibility for diagnosing disease and planning treatment. AI does not supplant the need for proper anamnesis, clinical examination, and final diagnosis and treatment planning. Nor does it replace clinical judgment. Over and underdiagnosis (and, therefore, treatment) of disease remains a concern, and the dentist’s responsibility for the appropriate prescription of imaging studies remains.
Transparency is an issue with AI, as the algorithms are proprietary information and do not guarantee that the results of AI-produced data are attuned to current standards of care. For example, caries detected via AI on bitewing radiographs does not imply that restoration of that tooth is indicated, nor does it assure that the carious lesion will not progress to the pulp prior to the subsequent examination if the tooth is not restored. The responsibility of confirming the diagnosis and formulating the appropriate treatment plan rests with the dentist.
Another ethical concern is whether AI impedes patient autonomy to accept or refuse treatment that may be “recommended” by AI. Will patients be intimidated and not contradict the “wisdom of the computer?” Further, should patients have to consent to have their images uploaded and reviewed by an AI program?
Related to the patient’s consent for the dentist to employ AI is the assurance that the privacy of the patient’s protected health information (PHI) will be maintained. This is a complex issue, as the datasets derived from deidentified patients’ radiographs are currently maintained in the AI providers’ servers for an indefinite period, without a mechanism for a patient to opt out or request that their data be deleted later. Moreover, as the images are subjected to deep learning algorithms, the results from each submitted examination reinforce the entire database.
It should be emphasized that the current iterations of AI for oral and maxillofacial radiology, overall, perhaps equal, but do not exceed the interpretive capabilities of humans. For the foreseeable future, AI-driven interpretation will support in-office diagnosis and treatment planning and improve workflow and patient education through the visual interfaces provided with the software. It is the responsibility of the dentist to avoid, wherever possible, over- and underdiagnosis and treatment.
Role of artificial intelligence in the diagnosis of caries, periodontal disease, and apical pathology
Traditionally, the detection of caries, periodontal disease, and apical pathology has relied on clinical examination supplemented by radiographic analysis. Advances in digital imaging and intraoral scanning have enhanced diagnostic capabilities, and the integration of AI now offers a transformative step forward in dental diagnostics. AI is playing an increasingly pivotal role by improving the accuracy, speed, and consistency of radiographic interpretation. Compared to traditional methods, AI-assisted analysis enhances early detection, reduces observer variability, and supports more precise and personalized treatment planning.
Caries
The role of AI in caries detection is most evident in image analysis, caries classification, and augmented diagnosis. AI models such as convolutional neural networks (CNNs) analyze bitewing and periapical radiographs to detect early enamel and dentinal lesions. These systems can classify lesions by severity—ranging from incipient to cavitated—and by location, such as occlusal, interproximal, or root surfaces. Furthermore, AI-assisted tools can highlight suspicious areas, helping clinicians minimize diagnostic oversight, especially in detecting early-stage lesions. The application of AI enhances diagnostic sensitivity and specificity, facilitates earlier detection of noncavitated lesions often missed during routine clinical examinations, and promotes greater consistency in diagnoses across practitioners.
Periodontal Disease
In the diagnosis of periodontal disease, the role of AI includes bone loss assessment, staging and grading, and progression prediction. AI can assess bone loss by quantifying alveolar bone loss on panoramic and periapical radiographs. Based on radiographic and clinical data such as pocket depth and attachment loss, AI can assist in the staging and grading of the patient’s periodontitis. Machine learning (ML) models in AI can also predict future disease progression based on the patient’s clinical history and radiographic data. The use of AI in the diagnosis of periodontal disease offers objective and reproducible measurements of bone loss, automated generation of periodontal charts saving valuable time for the clinician, and support in the treatment planning and risk stratification of the periodontal health of the patient.
Apical Pathology
AI is poised to give the ability for the clinician for enhanced lesion detection, lesion differentiation, and treatment monitoring in the diagnosis of apical pathology. AI algorithms are being developed to detect apical radiolucencies and hypodensities on periapical and CBCT images, oftentimes before they present clinically. Deep learning models are being developed to assist in the differential diagnosis of granulomas, cysts, and abscesses in CBCT images. AI-enhanced algorithms would have the capability to track changes in a lesion over time, which aids the clinician in posttreatment follow-up, improving patient outcomes. The benefits of AI in the diagnosis of apical lesions are early detection of asymptomatic infections, improved diagnostic confidence, and reduced interobserver variability.
Advantages and Limitations
There are many advantages to the use of AI in dental diagnostics. The reduction in subjectivity and interclinician variability improves the standardization of the diagnosis. Rapid image analysis offers faster clinical workflows improving the efficiency of the dental practice. AI can also offer a second opinion or flag suspicious findings, improving the diagnostic confidence of the clinician. Finally, AI can be used to help educate dental students with the interpretation of radiographic images. The use of AI has its limitations and challenges. AI requires increased dependence on high-quality annotated datasets. There may be integration challenges with the existing dental software and workflows in the dental office. The black-box nature of deep learning models may reduce trust among patients and clinicians. In addition, legal and ethical issues in diagnosis may arise without human oversight.
Artificial Intelligence Dental Companies
Pearl is one of the leading dental AI companies that develops AI solutions to support 2D and 3D radiographic diagnosis and practice management. Its flagship AI software, Second Opinion, is Federation Dentaire Internationale (FDI)-approved for the diagnosis of dental radiographs. Second Opinion automatically detects caries, bone loss, periapical pathology, and anatomic structures in real time during chairside examinations. Its diagnostic capabilities include detection of enamel and dentinal lesions, identification of subgingival and supragingival calculus deposits, detection of apical pathology, measurement of horizontal bone loss, and recognition of existing dental work.
Overjet AI is another prominent dental AI company that combines advanced image analysis, clinical decision support, patient education tools, and workflow automation into one Federation Dentaire Internationale (FDI)-cleared platform. Overjet IRIS is a cloud-based AI imaging suite that captures, enhances, and analyzes radiographs in one unified experience. The smart imaging software detects and outlines caries, bone loss, periapical lesions, and calculus and converts black and white radiographs into color-coded overlays. The colored visual overlays help patient better understand their oral health, and the insurance claim automation improves the efficiency of claims processing.
AI is rapidly transforming the field of dentistry by enhancing diagnostic accuracy, improving clinical efficiency, and supporting personalized treatment planning. AI-driven technologies—such as deep learning algorithms and CNNs—have shown substantial utility in the detection and management of dental diseases, including caries, periodontal disease, and apical pathology. These tools facilitate the early and accurate identification of lesions on radiographic images, assist in differential diagnosis, and offer objective assessments that help reduce interobserver variability. When integrated with clinical expertise, AI becomes a powerful adjunct that improves early detection, supports clinical decision-making, and standardizes diagnostic processes. Importantly, AI is not intended to replace clinical judgment but to augment it, enhancing both precision and consistency in dental diagnostics.
Artificial intelligence in oral and maxillofacial surgery
The emerging application of AI in oral and maxillofacial surgery is altering all aspects of care delivery and education through predictive, preventative, and personalized approaches. The improvement in computing power, exponential production, and data collection has led to the rapid development of AI-based tools reducing surgical errors, limiting cognitive bias, and supporting surgical reasoning. AI tools have multiple applications to enhance diagnostics, surgical planning, risk stratification, intraoperative decision making, and postoperative care. Increasing number of algorithms are now surpassing the capabilities of human experts for some very specific tasks but are not at the level to replace an experienced surgeon in overall care of the patient at the current moment. For example, neural networks have been used in the diagnosis of temporomandibular joint derangement; however with the current state of AI, the ultimate evaluation and management of the patient still lies with the surgeon. In oral and maxillofacial surgery, there are multiple potential applications of AI-based technologies; current developments in AI have had significant focus on dentoalveolar surgery, orthognathic surgery, and oral and maxillofacial pathology.
AI can aid surgical decision-making by integrating multiple factors to predict surgical outcomes and risks of complications. For example, an AI-based model can aid in determining surgical indications for the extraction of teeth by predicting the probability of tooth eruption risks or prediction of surgical difficulty and outcomes. AI-based predictive tools can identify patients at higher risk for inferior alveolar nerve injury by evaluation of panoramic radiographs. , Training an artificial neural network with specific clinical parameters can provide a predictive score for postoperative swelling after extraction of wisdom teeth. The use of robotic surgery and haptic technology is aided by AI to improve the accuracy of implant placement, dental extraction, or surgical resection margins. ML model can now predict dental implant failure and peri-implantitis as a tool for maximizing implant success. AI modeling and technologies can be used for trainees to have simulation that more closely mimics surgical conditions to practice surgical techniques and approaches in all aspects of the specialty including dentoalveolar surgery.
The care of patients with dentofacial deformity can benefit significantly from initial diagnosis all the way to postoperative management. AI-based tools integrating occlusion, cephalometric studies, and facial esthetics can help define the need for orthognathic surgery. , AI-supported segmentation can aid virtual surgical simulation and 3D printing to improve the efficiency of the surgical workflow. ML allows the rapid and reproducible identification and interpretation of the large number of hard tissue and soft tissue landmarks required for three-dimensional analysis of a patient. Soon, ML will identify preferences of individual surgeons, design surgical cuts, and personalize surgical plans without the need for a third party. Three-dimensional scanning technology will become diagnostic and incorporate virtual or augmented realities. Patient education tools such as predictive models of postoperative appearance will become more realistic. AI modeling has predicted the need for further orthognathic surgery in patients with cleft lip and palate based on the cleft type, the severity of lip separation at birth, the number of missing teeth, sex, the surgery methods of palatal closure, duration of orthodontic treatment, and data acquired from lateral cephalograms. Personalized recipes with AI assistance for enhanced recovery after surgery protocols will become the norm.
By leveraging AI-driven approaches for early detection, accurate diagnosis, and personalized treatment, we can significantly enhance patient outcomes in the management of oral and maxillofacial pathology. Machine and deep learning tools can assist in automated detection of pathologic lesions on maxillofacial radiographic imaging. AI-based imaging cancer detections neural network tools can make a significant impact on the early detection of premalignant and oral cancers by nonspecialists. Screening in primary care and early management of these lesions will thus be facilitated when these tools are released into clinical practice. Radiomic analysis or computational imaging allows the detection of metastatic lymph nodes based on training of digital parameters from the imaging, revealing texture, gray levels, and the relationship between voxels. Several AI algorithms can predict survival, risk of recurrence, or risk of postoperative complications. AI can predict treatment responses and long-term prognoses, aiding clinicians in making informed decisions and improving patient outcomes. The use of AI technologies will make a significant impact on the care delivery of patients with oral and maxillofacial pathology.
The implementation in daily practice of new technologies brought by AI needs to respond to certain technical, societal, and ethical limitations. ML algorithms require considerable volumes of data to carry out their training and to offer satisfactory performance. However, the protection of private health data must be ensured at all stages of the design, deployment, and use of the algorithms, according to the principle of “privacy by design”. Insurance companies with access to large datasets may use AI to identify practice patterns, low costs of care centers and surgeries, and surgeons with low complication rates. The challenges in the field of oral and maxillofacial surgery with using AI include limitations in large datasets, patient privacy concerns, and model generalizability. In the future, AI technologies will become ubiquitous in oral and maxillofacial surgery and will impact all aspects of the specialty.
AI in radiology for diagnostic prioritization, tasks related to reporting within picture archiving and communication systems
AI integration into picture archiving and communication systems (PACS) is transforming radiology workflows, particularly for diagnostic prioritization and reporting. The eventual goal would be to have a smooth transition to an AI-powered and automated diagnostic radiology workflow to enhance patient care and safety.
Diagnostic Prioritization
AI-driven Triage: AI algorithms analyze incoming studies in real time, identifying critical findings (eg, suspected stroke, pneumothorax, or intracranial hemorrhage) and prioritizing them for immediate radiologist attention. This ensures urgent cases are handled quickly, reducing delays in diagnosis and treatment. In terms of maxillofacial diagnostics, the algorithm is to detect maxillofacial abnormalities like jaw cyst or tumor detection as a priority. Dental caries and periodontal disease detection can also be automated using different software that already has the caries and periodontal disease module built into the workflow.
Worklist prioritization
AI can automatically flag urgent cases and push them to the top of radiologists’ worklists, allowing radiologists to focus on complex cases while ensuring timely review of high-risk conditions. This is true for cases needing to be isolated for jaw cyst and tumor detection compared to those with only caries and periodontal bone loss.
Tasks Related to Reporting Within Picture Archiving and Communication Systems
Automated data extraction and report drafting
AI-powered systems can extract structured data from imaging studies (eg, measurements and findings) and generate preliminary reports for radiologists to review and finalize. AI facilitates structured reporting by auto-filling templates based on image findings and tagging images for future reference or education. This saves time, improves communication clarity, and ensures consistency. AI tools with natural language processing (NLP) can convert spoken or written notes into structured reports, reducing the burden of manual dictation and minimizing clerical errors. Some platforms offer AI-generated report summaries and explanations, which can reduce reporting time and improve consistency. AI can automate the process of generating billing codes based on the findings in radiology reports.
Benefits of Artificial Intelligence in Diagnostic Prioritization and Reporting
AI automates repetitive tasks, streamlines workflows, and helps manage rising imaging volumes. Prioritization and automation reduce the time from image acquisition to diagnosis and report generation. AI alleviates the burden of heavy workloads and administrative tasks, allowing radiologists to focus on complex cases and clinical decision-making. AI can identify subtle abnormalities that might be missed by the human eye and improve consistency in interpretation.
Radiologists can use AI to validate their interpretations and feel more confident in their diagnoses. Faster and more accurate diagnoses lead to earlier intervention and improved treatment outcomes. There are several challenges. AI models require large, diverse, and high-quality datasets to ensure accurate and unbiased performance. Seamless integration with existing PACS, Radiology Information Systems, and electronic health record (EHR) systems is crucial to avoid workflow disruption. Radiologists and staff need proper training to effectively use AI tools and understand their limitations. Addressing issues of data privacy, algorithmic transparency, and accountability is essential.
AI is becoming an essential tool in modern radiology, offering significant advantages in diagnostic prioritization and reporting within PACS systems. RamSoft’s OmegaAI and PowerServer platforms are examples of solutions that are integrating AI to streamline workflows and enhance patient care.
AI is reshaping the future of healthcare—and radiology is at the forefront of that transformation. As imaging volumes grow and diagnostic demands become more complex, radiologists are turning to AI to work smarter, not harder. From detecting subtle abnormalities to prioritizing urgent cases, AI is proving to be a powerful tool that enhances clinical accuracy, speeds up workflows, and ultimately improves patient outcomes.
Across the globe, radiology practices are adopting AI-driven technologies to streamline image interpretation, reduce burnout, and make data-driven decisions with greater confidence. But to truly unlock AI’s full potential, it needs to be seamlessly integrated into daily workflows—not added as another layer of difficulty. Companies like Ramsoft who are leaders in cloud-based radiology solutions, they are committed to helping practices harness AI in ways that are practical, scalable, and impactful. Through innovative partnerships and built-in AI capabilities, platforms like our PowerServer and OmegaAI platforms empower radiologists with tools that support—not replace—their expertise.
Machine Learning: Improving Diagnostic Accuracy
ML is a key branch of AI that enables computers to learn from data and improve their performance without being explicitly programmed. In health care, this means AI algorithms can analyze thousands—sometimes millions—of anonymized medical images to detect patterns, identify anomalies, and generate insights. As these algorithms train on more diverse and representative datasets, their ability to support radiologists with faster, more accurate diagnoses improves significantly.
This continuous learning process enhances diagnostic confidence, helps reduce human error, and contributes to faster, more patient-centered care. It is not about replacing radiologists—it is about equipping them with powerful tools to work smarter and faster. AI has the power to transform health care—and integrating ML into cloud-based and cloud-native platforms makes that transformation real. With smarter tools and streamlined workflows, providers gain the insight and efficiency they need to deliver high-quality care in a fast-moving, data-driven world.
Stay updated, free dental videos. Join our Telegram channel
VIDEdental - Online dental courses
