1.1. Definition and Scope

Artificial intelligence (AI) in endodontics refers to applying advanced computer algorithms and machine-learning techniques to improve endodontic disease diagnosis, treatment, and overall management. AI encompasses a range of technologies, including neural networks, deep learning, and natural language processing, which can analyze vast amounts of data and learn from patterns to make informed decisions or predictions without explicit human programing [1]. In endodontics, AI is employed to enhance the precision of diagnostic tools, optimize treatment planning, predict outcomes, and even assist in surgical procedures [2].

1.2. Research Gaps in AI Applications in Endodontics

Despite the promising advancements in AI for endodontics, several significant research gaps remain. Firstly, there is limited clinical validation of AI algorithms, as many studies focus on in vitro or retrospective data without extensive real-world application. Secondly, the integration of AI into routine endodontic practice is hindered by a lack of standardized protocols and interoperability with existing clinical workflows. Another critical gap lies in the ethical and legal considerations, particularly concerning data privacy, algorithmic transparency, and potential biases in AI models. Furthermore, while AI excels in diagnostic imaging and predictive analytics, its potential in procedural assistance and patient-specific treatment customization remains underexplored. These gaps emphasize the need for collaborative efforts across academia, industry, and clinical settings to enhance the applicability and reliability of AI in endodontics.

1.3. Objectives of the Review

The primary objective of this review is to provide a comprehensive analysis of the current applications of AI in endodontics, focusing on diagnostic imaging, predictive analytics, and treatment planning. The review aims to synthesize existing evidence to evaluate the benefits and limitations of AI technologies in this domain. Additionally, it seeks to identify challenges and propose potential strategies to address the integration, ethical, and validation issues. By highlighting research gaps and exploring future directions, this review aims to serve as a resource for clinicians, researchers, and policymakers interested in leveraging AI to advance endodontic practice.

This manuscript is structured to provide a comprehensive overview of AI in endodontics. The next section begins with a historical overview of the evolution of endodontic practices, transitioning from traditional to digital and AI-enhanced methodologies. Following this, we delve into the fundamental concepts of AI and its key technologies, such as machine learning (ML), neural networks, and deep learning, relevant to the field of endodontics. Subsequently, the applications of AI are explored in detail, including its role in diagnostic imaging, predictive analytics, and procedural assistance. A section on challenges and limitations addresses ethical considerations, data privacy concerns, and integration barriers. Finally, the manuscript discusses future directions, focusing on emerging technologies like augmented reality (AR), robotics, and telehealth and their potential impact on endodontic practice. The conclusion summarizes key insights and emphasizes the transformative potential of AI in enhancing patient care and outcomes in endodontics.

2.1. The Transition From Traditional to Digital

Significant milestones have marked the evolution from traditional to digital endodontics. The introduction of digital radiography revolutionized diagnostic imaging, offering enhanced image quality and reduced radiation exposure. Advances in computer-aided design and manufacturing (CAD/CAM) have facilitated the precise fabrication of endodontic instruments and prosthetics, improving treatment efficiency and patient outcomes. Integrating cone-beam computed tomography (CBCT) provides detailed 3D images, aiding in the accurate diagnosis and planning of complex cases. These technological advancements have laid the foundation for the integration of AI, further enhancing diagnostic and therapeutic capabilities.

Integrating AI into endodontics is a relatively recent development that is building on the broader adoption of digital technologies in dentistry. The journey began with digitizing patient records and imaging, which laid the groundwork for more sophisticated AI applications. Early AI systems in dentistry focused on essential diagnostic support and simple automation tasks. However, advancements in ML and data analytics have significantly expanded the capabilities of AI in this field [3].

By the 2010s, AI applications showed promising results in interpreting complex dental images such as radiographs and CBCT scans. Researchers have developed algorithms capable of identifying anatomical structures and pathologies with high accuracy, thus aiding the detection of root fractures, periapical lesions, and other endodontic issues [4].

In recent years, AI has penetrated endodontics by developing predictive models that can predict treatment outcomes and potential complications. These advancements have been driven by collaborative efforts among data scientists, engineers, and dental professionals, resulting in AI systems that are increasingly integrated into clinical workflows [5].

2.2. Importance and Impact on Modern Endodontics

AI has the potential to revolutionize endodontics by enhancing the precision, efficiency, and effectiveness of dental care. One of the primary benefits is an improvement in diagnostic accuracy. AI systems analyze dental images with speed and accuracy, reducing the risk of misdiagnosis and ensuring early detection of conditions [6].

Moreover, AI facilitates personalized treatment planning by analyzing patient-specific data to recommend the most appropriate intervention. This tailored approach improves patient outcomes and enhances care delivery efficiency, potentially reducing the time and costs associated with endodontic treatments [7].

AI’s predictive analytic capabilities are particularly valuable in endodontics. By evaluating factors like patient history, clinical findings, and treatment responses, AI predicts treatment success and identifies potential complications. This foresight allows clinicians to make informed decisions and proactively address issues before they become problematic [8].

AI not only improves clinical practice but also advances endodontic research and education. AI-driven data analysis can provide new insights into disease patterns and treatment efficacies, guiding future research directions. For educational purposes, AI-powered simulators and training programs provide dental students and professionals with realistic hands-on experiences that enhance their skills and knowledge [5].

In summary, AI in endodontics represents a transformative leap forward that significantly improves diagnostic accuracy, treatment planning, and patient care. As AI technology continues to evolve, its integration into endodontic practices is expected to deepen, leading to even more significant advancements.

3.1. Basic Concepts of AI

AI simulates human intelligence processes using machines, particularly computer systems [9]. These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction [10]. Designed to perform tasks requiring human intelligence, AI excels in visual perception, speech recognition, decision-making, and language translation [11].

3.2. Types of AI

Narrow AI, or weak AI, is designed and trained to perform a specific task. Virtual personal assistants, such as Apple’s Siri and Amazon’s Alexa, are examples of narrow AI. These systems operate within a predefined range of functions, focusing on one task with a high proficiency [12].

General AI, also known as strong AI or artificial general intelligence (AGI), refers to systems that possess the ability to understand, learn, and apply knowledge in a way that is indistinguishable from human intelligence. General AI aims to perform any intellectual task that a human can perform, but this level of AI remains largely theoretical and has not yet been realized [13].

Machine learning (ML): ML is a subset of AI that uses algorithms and statistical models to enable computers to perform specific tasks without using explicit instructions. Instead, they rely on patterns and inferences. ML algorithms build a model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programed to perform the task [14].

Deep learning: Deep learning is a subset of ML involving three or more layers of neural networks. These neural networks attempt to simulate the behavior of the human brain to “learn” from large amounts of data. Deep learning techniques have revolutionized fields such as image and speech recognition [15].

3.3. Key Technologies Used in AI

Neural networks: Neural networks are a series of algorithms that mimic the human brain’s operations to recognize relationships between vast amounts of data. They consist of layers of nodes, each of which performs simple computation. The results of these computations are passed to the next layer of nodes, and this process continues until the network produces an output. Neural networks are instrumental in speech recognition, image analysis, and medical diagnosis applications [16].

Algorithms: Algorithms are step-by-step procedures or formulas used to solve a problem. In AI, algorithms are used to process and learn from data. Standard AI algorithms include decision trees, vector machines, and clustering algorithms. These algorithms enable machines to identify patterns in data, make decisions, and predict future outcomes [17].

Data analytics: Data analytics involves the examination of large datasets to uncover hidden patterns, correlations, and other insights. In AI, data analytics is critical because the quality and quantity of data available significantly affect the performance of AI models. Techniques such as data mining, statistical analysis, and predictive modeling are used to analyze the data and draw meaningful conclusions that drive AI applications [18].

3.4. Focus on Healthcare and Dentistry

AI in healthcare and dentistry is transforming patient care through its ability to analyze large datasets and provide insights. In endodontics, AI systems enhance diagnostic accuracy by precisely interpreting complex radiographic images and identifying pathologies. ML models predict treatment outcomes based on patient data, aiding clinicians in personalized treatment planning. These applications streamline workflows and support clinical decision-making, demonstrating AI’s potential to improve patient care and outcomes in dentistry.

4.1. Diagnostic Imaging

AI has significantly advanced diagnostic imaging in endodontics by enhancing the accuracy and efficiency of radiographic analysis. AI systems, particularly those using ML and deep learning algorithms, can identify and interpret complex patterns in medical images that may not be easily discernible by the human eye [19]. This capability is crucial in endodontics, where precise diagnosis is essential for effective treatment planning and outcomes.

4.2. Enhancements in Radiographic Analysis

AI has improved radiographic analyses in several ways. With high accuracy, ML algorithms can be trained to detect periapical lesions, root fractures, and other pathologies on dental radiographs. Studies have shown that AI can achieve a diagnostic performance comparable to or surpass that of human experts. A study by Orhan et al. [20] demonstrated that a deep learning-based AI system could accurately detect periapical lesions on panoramic radiographs, significantly aiding early diagnosis and intervention.

4.3. AI in CBCT Interpretation

CBCT has become an indispensable tool in endodontics, providing detailed three-dimensional images of dental and maxillofacial structures. AI enhances CBCT interpretation by automating the detection of anatomical features and pathologies, reducing the risk of human error and saving time. For instance, algorithms can automatically segment and analyze root canal systems, assess bone density, and identify periapical lesions with high precision [21].

4.4. Predictive Analytics

Predictive analytics in AI uses historical data to predict future outcomes. In endodontics, AI-driven predictive analytics can predict the prognosis of endodontic treatments and potential complications [22].

4.5. Prognosis of Endodontic Treatments

AI models can analyze large datasets from past treatments to predict the likelihood of the success or failure of endodontic procedures. These models can provide personalized prognostic insights by considering patient demographics, clinical findings, and procedural details. A review by Karobari et al. demonstrated the potential of AI in predicting endodontic treatment outcomes by analyzing factors influencing the success rates of root canal treatments [23].

4.6. Predicting Treatment Outcomes

AI systems can be used to predict treatment outcomes with remarkable accuracy. ML algorithms can process and analyze patient data, including radiographic images and clinical records, to predict the success of endodontic treatment. This predictive capability allows clinicians to make more informed decisions, optimize treatment plans, and enhance patient outcomes [24]. A study by Zargar et al. [25] found that AI models could effectively predict the healing outcomes of apical periodontitis following endodontic therapy.

4.7. Automated Clinical Decision Support

AI-assisted clinical decision support systems (CDSS) are designed to aid clinicians in making evidence-based decisions by providing real-time analysis and recommendations [26].

4.8. AI-Assisted Diagnosis

AI can assist in diagnosing endodontic conditions by analyzing patient data and identifying patterns that may indicate specific pathologies. For example, AI algorithms can detect early signs of pulpitis or apical periodontitis, allowing for timely intervention and improved patient care. Zheng et al. demonstrated that AI could accurately diagnose pulp diseases by analyzing the clinical symptoms and radiographic findings [27].

4.9. Treatment Planning Assistance

AI can also aid treatment planning by suggesting optimal treatment strategies based on individual patient data. These include recommending the appropriate type of root canal treatment, estimating the necessary length and number of visits, and identifying potential complications. AI-driven treatment planning systems can enhance the efficiency and effectiveness of endodontic care, leading to improved patient outcomes [8].

5.1. AI-Assisted Root Canal Treatment

AI technologies are revolutionizing root canal treatment to enhance precision and efficiency. AI systems can analyze preoperative radiographs and CBCT scans to provide detailed visualization of the tooth’s internal structure, aiding in identifying the root canal anatomy. This enables endodontists to plan and execute root canal procedures more accurately, potentially reducing the risk of treatment failure and complications [23, 28].

5.2. Locating Root Canals

One of the critical challenges in root canal therapy is the accurate location of all root canals, particularly in teeth with complex anatomies. AI algorithms trained on extensive datasets of dental images can assist in identifying root canal orifices that might be difficult to detect visually. AI can enhance the accuracy of initial diagnoses by providing AR overlays on digital images, guiding clinicians to locate canals more reliably [28, 29]. Studies have shown that AI can significantly improve the detection rate of accessory canals and variations in root canal morphology [30, 31].

5.3. Determining Working Length

Accurate determination of working length is essential for successful root canal treatment. AI technologies, including machine-learning models, can rapidly process radiographic images to estimate the working length. These systems can automatically measure the distance from the coronal reference point to the apical foramen, reducing reliance on manual calculations and minimizing the risk of over—or under instrumentation. A study by Saghiri et al. demonstrated that AI was 100% accurate in determining root length when compared to the actual measurement following tooth extraction, offering a reliable alternative for endodontists [32].

5.4. Laser and Optical Technologies

AI has also enhanced the application of laser and optical technologies in endodontics. AI-powered laser systems can remove infected tissue and disinfect root canals permanently. These systems can adapt the laser’s intensity and focus based on real-time feedback, ensuring optimal effectiveness while minimizing damage to surrounding healthy tissues [33, 34]. Optical coherence tomography (OCT) and AI can provide high-resolution imaging of root canal systems, allowing for better visualization and assessment during treatment [35].

6.1. Smart Materials

AI advancements have facilitated the development of smart materials tailored for endodontic applications. These materials, equipped with embedded sensors and actuators, can respond intelligently to environmental stimuli, enhancing their functionality and performance. AI algorithms play a crucial role in optimizing the design and composition of smart materials, allowing for precise control over their properties, such as mechanical strength, bioactivity, and antimicrobial activity [36]. For example, AI-driven modeling techniques predict material behavior under different conditions, facilitating the customization of smart materials to meet specific clinical requirements [23].

6.2. Nanotechnology Applications

Nanotechnology has emerged as a promising area of research in endodontics, as it offers innovative solutions for developing advanced materials with superior properties. AI algorithms have been employed to design and characterize nanomaterials with precise control over their size, morphology, and surface chemistry. Nanoparticles incorporated into endodontic materials, such as root canal sealers and irrigants, exhibit enhanced antimicrobial efficacy, biocompatibility, and tissue regenerative potential [37]. AI-driven simulations aid in the optimization of nanoparticle formulations, ensuring optimal drug delivery and therapeutic outcomes [38].

6.3. Biocompatible Materials Development

AI-driven approaches have revolutionized the development of biocompatible materials for endodontic applications, thereby addressing the growing demand for safe and biologically compatible dental products. By leveraging ML algorithms, researchers can predict the biocompatibility of materials based on their chemical composition, surface characteristics, and interactions with host tissues [23, 28]. This enables the rapid screening of candidate materials and the identification of novel biomaterial formulations with enhanced biocompatibility profiles. AI-guided optimization of material properties such as surface roughness, wettability, and cytotoxicity ensures the successful integration of endodontic materials into the oral environment while minimizing adverse reactions [28].

While AI offers significant benefits in analyzing dental images, it is crucial to acknowledge the variability and complexity of clinical scenarios. AI systems excel in pattern recognition and processing vast datasets, but they may struggle with nuanced cases that require clinical judgment and expertise. Current literature highlights the need for human oversight, emphasizing that AI should augment rather than replace clinician decision-making. Combining AI and human expertise can lead to more accurate and comprehensive diagnoses in complex cases.

7.1. Increased Accuracy and Precision

AI technologies offer unprecedented accuracy and precision in various aspects of endodontic practices. AI algorithms can analyze complex datasets from diagnostic imaging, such as CBCT scans, to detect subtle anatomical variations, identify pathologies, and precisely locate root canal morphology. By automating image interpretation and analysis, AI reduces the risk of human error and enhances diagnostic confidence, thereby leading to more accurate treatment planning and execution [23, 28, 29].

7.2. Improved Patient Outcomes

Integrating AI-driven tools and technologies into endodontic practice has improved patient outcomes and treatment efficacy. AI algorithms can predict treatment outcomes, assess the prognosis of endodontic interventions, and personalize treatment plans based on patient-specific factors [23]. By optimizing treatment protocols and predicting potential complications, AI enhances treatment success rates and patient satisfaction, ultimately improving oral health outcomes for individuals undergoing endodontic procedures [28].

7.3. Time and Cost Efficiency

AI-enabled automation streamlines various aspects of endodontic practice, resulting in significant time and cost savings for practitioners and patients. AI algorithms can automate repetitive tasks such as image analysis, treatment planning, and documentation, reducing the time required for each procedure [29, 39]. Moreover, AI-driven predictive analytics can optimize resource allocation, minimize treatment delays, and prevent unnecessary interventions, leading to more efficient use of healthcare resources and reduced healthcare costs [40].

7.4. Enhanced Training and Education for Practitioners

AI technology plays a crucial role in enhancing the training and education of endodontic practitioners, facilitating skill development, and improving clinical decision-making. AI-driven simulation platforms provide realistic virtual environments for trainees to practice various endodontic procedures, refine their techniques, and gain hands-on experience in risk-free settings. In addition, AI-powered educational tools offer personalized learning modules, real-time feedback, and performance analytics, enabling continuous professional development and competency assessment among endodontic professionals [23, 39, 40].

8.1. Data Privacy and Security Concerns

Integrating AI into endodontic practice raises significant data privacy and security concerns. AI algorithms rely on large datasets, including patient health records and diagnostic images, to train and improve performance [41]. However, the storage, transmission, and analysis of sensitive patient information pose risks of data breaches, unauthorized access, and privacy violations. Ensuring robust encryption protocols, access controls, and data anonymization techniques are essential to safeguard patient confidentiality and comply with regulatory requirements such as the Health Insurance Portability and Accountability Act (HIPAA) [41].

8.2. Ethical Considerations

Ethical considerations regarding using AI in endodontics include patient autonomy, informed consent, and algorithmic bias. AI algorithms may inadvertently perpetuate biases in training data, leading to disparities in diagnostic accuracy and treatment recommendations across demographic groups [42, 43]. Moreover, using AI-driven decision support systems raises concerns about the delegation of clinical responsibility and the potential for overreliance on algorithmic predictions. Practitioners must maintain transparency, accountability, and human oversight when utilizing AI technologies to ensure ethical and equitable patient care [44].

Data privacy is also a significant concern, as AI systems require access to sensitive patient information. Ensuring robust data protection measures and patient consent protocols is essential to maintain confidentiality. Algorithmic bias is another ethical issue where AI systems may inadvertently perpetuate existing disparities in healthcare. Addressing these biases through diverse training datasets and continuous monitoring is crucial. Additionally, practical challenges such as the cost of AI implementation, the need for clinician training, and resistance to technology adoption must be addressed to facilitate seamless integration into clinical practice [42–44].

8.3. Reliability and Trustworthiness of AI Systems

The reliability and trustworthiness of AI systems pose significant challenges in endodontic practice, particularly in algorithmic robustness, interpretability, and validation. AI algorithms may exhibit unpredictable behavior, susceptibility to adversarial attacks, or limitations in generalizability when applied to real-world clinical scenarios [23, 28, 29]. Additionally, the lack of transparency and interpretability in complex deep learning models hinders clinicians’ ability to understand and trust algorithmic predictions [45]. Rigorous validation studies, transparency in algorithmic decision-making, and ongoing monitoring of AI performance are essential to ensure the reliability and trustworthiness of AI systems in endodontics [46].

While AI can enhance diagnostic accuracy, it is not infallible and may exhibit unpredictable behaviors, especially when encountering novel or complex cases. The interpretability of AI systems is another limitation, as clinicians need to understand the reasoning behind AI-driven recommendations to make informed decisions. These challenges highlight the importance of developing AI systems that are transparent, reliable, and capable of working alongside clinicians to improve patient care [45, 46].

8.4. Integration With Existing Clinical Practices

Integrating AI into existing clinical practices challenges workflow integration, usability, and user acceptance. Clinicians may be reluctant to adopt AI technologies due to perceived disruptions in established workflows, concerns about job displacement, or unfamiliarity with AI-driven tools [47]. Moreover, the interoperability of AI systems with existing electronic health record (EHR) systems and imaging modalities may present technical barriers and compatibility issues. Practical training, education, and change management strategies are essential to facilitate the seamless integration of AI into endodontic workflows and to promote user acceptance among clinicians [48].

Integrating AI in endodontics presents several challenges, including data security and algorithmic biases. Data breaches pose a significant risk, as AI systems rely on large datasets that must be protected to maintain patient confidentiality. Algorithmic biases can arise if the AI models are trained on nonrepresentative datasets, leading to skewed results and potentially unequal treatment outcomes. For instance, if an AI model is trained primarily on data from one demographic group, it may perform less accurately for other groups, exacerbating existing healthcare disparities. Addressing these challenges is crucial to ensure AI’s safe and equitable use in clinical practice [47, 48].

8.5. Proposed Framework for AI Integration in Clinical Practice [23, 28, 29]

8.6. Future Directions of AI in Endodontics

Future directions in AI for endodontics include exploring the potential of personalized care. AI systems can analyze patient data to tailor treatment plans, optimizing outcomes based on unique characteristics and preferences. Emerging technologies like 3D printing offer opportunities for creating patient-specific endodontic instruments and prosthetics, further enhancing personalized care.

The future of AI in endodontics holds promise for developing and integrating innovative technologies that enhance diagnostic accuracy, treatment planning, and patient outcomes. Emerging technologies, such as AR, virtual reality (VR), and 3D printing, are poised to revolutionize endodontic practice by providing immersive visualization, patient-specific treatment modalities, and customized therapeutic interventions [8].

Integrating AR and VR technologies in endodontics holds promise for clinical practice and education. AR can enhance real-time visualization during complex procedures, providing clinicians with overlaying anatomical structures and treatment pathways. VR offers immersive training simulations, allowing practitioners to hone their skills in a risk-free environment. Augmented by AI, telehealth platforms facilitate remote consultations and diagnostics, improving access to endodontic care for patients in underserved areas. These emerging technologies can transform endodontic practice, making it more efficient, accessible, and effective.

8.7. Emerging Trends and Innovations [3–7]

8.8. Ongoing Research and Development

Ongoing research and development efforts in AI-driven endodontics focus on advancing ML algorithms, deep learning architectures, and natural language processing techniques to extract actionable insights from large-scale clinical datasets [49]. Collaborative initiatives between academia, industry, and healthcare institutions are driving innovation in image analysis, predictive modeling, and clinical decision support systems tailored to the unique challenges and requirements of endodontic care [50].

8.9. Integration of Robotics in Endodontic Procedures [33]

Robotics, coupled with AI, is gradually transforming endodontic practice. Robotic systems such as Yomi (FDA-approved for dental implant surgeries) are being adapted for endodontics to perform precise canal access, shaping, and cleaning. These systems leverage AI to analyze CBCT data, providing real-time feedback and adjustment capabilities during procedures.

For example, automated robotic systems can navigate challenging anatomical variations, such as calcified canals, with unparalleled precision, minimizing procedural risks. Additionally, integrating robotics with AR allows clinicians to visualize treatment pathways in real time, improving outcomes in complex cases.

8.10. AI-Driven Predictive Analytics and Treatment Planning

Predictive analytics, powered by ML, provides personalized insights into endodontic treatment outcomes. By analyzing patient-specific data, such as clinical history and imaging results, ML algorithms can predict procedural success rates and potential complications. Karobari et al. highlighted AI’s capability to assess the long-term prognosis of root canal treatments by evaluating risk factors like patient age, lesion size, and systemic health [23].

AI also supports dynamic treatment planning by suggesting optimal intervention strategies based on real-time clinical inputs. This approach not only enhances decision-making but also streamlines procedural workflows, reducing chair-side time and improving patient satisfaction.

8.11. Potential for Personalized Endodontic Care

The integration of AI into endodontic practice has the potential to facilitate personalized and precision-based treatment approaches tailored to individual patient needs and characteristics. AI-driven predictive analytics, genomic profiling, and patient risk stratification algorithms enable clinicians to optimize treatment outcomes, minimize procedural risks, and enhance patient satisfaction through targeted interventions [2, 28]. By harnessing the power of AI, endodontists can deliver customized treatment plans that account for patient preferences, medical histories, and prognostic factors, thereby ushering in a new era of personalized endodontic care [23].

8.12. Trends and Innovations on the Horizon

Several trends and innovations are on the horizon for AI in endodontics, including integrating telehealth platforms, wearable devices, and mobile applications for remote patient monitoring, self-care management, and teleconsultation [2]. Additionally, advancements in robotics, automation, and minimally invasive techniques hold promise for streamlining endodontic procedures, improving treatment precision, and reducing patient discomfort [51]. Furthermore, the convergence of AI with other cutting-edge technologies such as blockchain, internet of medical things (IoMT), and quantum computing is poised to unlock new opportunities for data-driven insights, decentralized healthcare delivery, and real-time decision support in endodontic practice [52].

8.13. Regulatory and Legal Considerations of AI in Healthcare

Integrating AI technologies in healthcare, including endodontics, presents significant regulatory and legal challenges. As AI systems are increasingly used for diagnosis, treatment planning, and decision-making, ensuring their safe and effective deployment is paramount. Below, we delve into the current regulatory landscape, challenges in obtaining approval, and the legal implications of AI-driven decisions in clinical practice.

8.14. Current Regulatory Landscape

8.15. Challenges in Obtaining Regulatory Approval

8.16. Legal Implications of AI-Driven Decisions

To provide a comprehensive understanding of the various AI technologies utilized in endodontics, their functionalities, advantages, limitations, and clinical applications have been summarized in Table 1. This comparative overview serves to highlight the unique contributions of each technology and their potential to transform endodontic practice. By examining their specific use cases, the table facilitates a clearer understanding of how these technologies address diagnostic, predictive, and procedural challenges in clinical settings.