Introduction

Advancements in dental restoration have significantly improved patient outcomes over the years. However, despite the evolution of materials and techniques, predicting the long-term success of dental restorations remains a challenge. Factors such as material properties, patient-specific conditions, and clinician expertise influence restoration longevity. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in healthcare, offering predictive capabilities that hold promise for revolutionizing dental practices. The application of AI in dentistry is not merely a futuristic concept but a present-day reality that seeks to enhance precision and reliability in treatment planning and execution (1).

AI and ML leverage large datasets to identify patterns and generate predictions, enabling clinicians to make data-driven decisions. In dental restoration, these technologies analyze factors such as occlusal forces, material degradation, and patient habits to predict restoration success. Unlike traditional approaches reliant on clinician experience and generalized guidelines, AI provides personalized insights that can improve treatment outcomes (2). Additionally, machine learning algorithms can integrate patient-specific data with material science knowledge, allowing for a holistic approach to predicting restoration longevity. One key advantage of AI in this domain is its ability to process and analyze complex datasets from diverse sources. For example, AI tools can utilize clinical photographs, radiographs, and electronic health records to develop predictive models. These capabilities can assist in identifying potential complications, thereby reducing the likelihood of restoration failure. Moreover, by incorporating AI into digital workflows, clinicians can optimize chairside efficiency while maintaining high standards of care (3).

While the potential of AI in dentistry is promising, challenges persist. Ethical considerations, data privacy concerns, and the need for standardized datasets pose barriers to widespread adoption. Additionally, integrating AI systems into clinical settings requires substantial investment and training, which may deter smaller practices. Despite these hurdles, the growing body of literature underscores the transformative potential of AI and ML in improving dental restoration outcomes (3, 4). This review aims to explore the impact of AI and machine learning in predicting the success of dental restorations.

Review

The application of AI and machine learning in predicting the success of dental restorations has garnered significant attention due to its potential to revolutionize clinical practices. AI-driven tools analyze extensive datasets encompassing patient-specific factors, material properties, and procedural variables, enabling clinicians to make data-informed decisions. For instance, AI systems can assess occlusal forces and predict stress distributions in restorations, thus identifying risks of failure early in the treatment process (5). These predictive capabilities not only enhance the precision of restorations but also improve patient satisfaction by reducing the need for revisions. Moreover, machine learning algorithms contribute to personalized treatment planning. By analyzing variables such as a patient’s oral hygiene habits and genetic predispositions, these systems can offer tailored recommendations for restorative procedures. Such insights empower clinicians to optimize treatment strategies and enhance the longevity of restorations. However, despite these advancements, challenges persist. The integration of AI into clinical workflows demands robust datasets, which are often limited in diversity and standardization. Additionally, concerns related to data privacy and the cost of implementing AI systems hinder widespread adoption (6). Future research should focus on addressing these limitations by fostering collaboration between AI developers and dental professionals to enhance the applicability and reliability of these technologies.

Applications of AI in Analyzing Dental Restoration Success Rates

AI has been instrumental in redefining the evaluation and prediction of dental restoration outcomes, providing unparalleled accuracy and efficiency in decision-making. By leveraging computational algorithms, AI systems analyze intricate datasets comprising patient demographics, clinical history, imaging modalities, and material characteristics. These systems generate predictive models that empower clinicians to foresee potential complications, thereby enhancing the reliability and durability of restorations (7).

One of the core applications of AI lies in its ability to assess the biomechanical behavior of restorative materials under varying oral conditions. For example, finite element analysis combined with machine learning can simulate stress and strain patterns in dental restorations, providing insights into material wear and failure points. This predictive capacity enables practitioners to select materials best suited for specific cases, reducing the risk of fractures or debonding over time (8). Moreover, these models help tailor the restoration process to each patient’s unique oral environment, offering a level of customization previously unattainable. AI also plays a pivotal role in analyzing imaging data for dental restorations. Machine learning algorithms process data from digital radiographs, cone-beam computed tomography, and intraoral scans to detect defects in restorations, such as microleakages, marginal gaps, or cracks that may not be visible to the human eye. This capability ensures early intervention and corrective measures, ultimately prolonging restoration longevity (9). Furthermore, advancements in AI-powered imaging tools allow for real-time evaluation of restorations during clinical procedures, assisting dentists in making precise intraoperative decisions.

Another significant area of AI application is the evaluation of patient-specific factors contributing to restoration success. AI systems analyze lifestyle habits, genetic predispositions, and oral hygiene patterns to predict potential risks. For instance, studies have shown that AI can evaluate occlusal loading patterns and bruxism tendencies, predicting their impact on restoration outcomes (7). These insights provide clinicians with actionable data to devise preventive strategies, such as using protective occlusal guards or adjusting material selection for high-risk patients. In addition, AI tools have begun to integrate with digital workflows in restorative dentistry. From CAD/CAM systems to digital impression-taking, AI algorithms streamline treatment planning and execution. For example, algorithms can identify optimal crown designs or suggest modifications to ensure proper fit and function. These technologies reduce chairside time while maintaining high accuracy, benefiting both clinicians and patients. Furthermore, AI-driven software enhances the reproducibility of outcomes, minimizing variability caused by human error. Despite these advancements, challenges remain in the application of AI for dental restoration analysis. AI models require large, diverse datasets for training and validation, which are often scarce due to inconsistencies in clinical documentation and imaging formats. Ethical considerations related to data ownership and patient consent further complicate the adoption of AI technologies in dental practice. Nonetheless, the integration of AI continues to grow, supported by ongoing innovations and collaborative efforts between researchers and clinicians (10).

Machine Learning Algorithms for Predictive Modeling in Dentistry

ML algorithms have transformed the predictive modeling landscape in dentistry by offering tools that can analyze vast amounts of data to identify trends, patterns, and outcomes. These algorithms, which encompass supervised, unsupervised, and reinforcement learning models, enable clinicians to predict the success of dental restorations with higher accuracy than traditional approaches. In predictive modeling, ML processes datasets from clinical records, diagnostic images, and laboratory test results to uncover correlations that may not be immediately apparent to practitioners (11).

Supervised learning algorithms, such as decision trees, random forests, and support vector machines, are widely used in dentistry to predict treatment outcomes. These models are trained using labeled datasets that include information about patient demographics, oral health conditions, and restoration types. For example, random forests can analyze a combination of patient-specific factors, including bone density and periodontal health, to predict implant success rates. Similarly, support vector machines have been employed to classify restoration failure modes based on data from digital imaging and radiography (12). The application of these models not only aids in risk assessment but also supports clinical decision-making by identifying optimal treatment options for individual patients.

Deep learning, a subset of ML, has shown exceptional promise in dentistry, particularly in processing complex datasets such as 3D imaging and intraoral scans. Convolutional neural networks (CNNs) are frequently employed to detect patterns in imaging data, identifying issues like caries progression, fractures, or marginal discrepancies in restorations. These algorithms excel in image recognition tasks, learning from pixel-level information to differentiate between intact and compromised restorations. Furthermore, recurrent neural networks are used to analyze temporal data, such as patient follow-up records, to predict long-term restoration success. These advanced models facilitate personalized treatment plans by dynamically incorporating new data into their predictions (13).

Unsupervised learning algorithms, including clustering and dimensionality reduction techniques, contribute to understanding complex interrelationships within dental datasets. These models group patients with similar oral health profiles, enabling clinicians to develop targeted treatment strategies. For example, clustering techniques have been used to categorize patients based on their risk of restoration failure, identifying high-risk groups that may benefit from enhanced follow-up protocols. Additionally, dimensionality reduction techniques such as principal component analysis simplify large datasets by highlighting the most critical factors influencing restoration outcomes, aiding researchers and practitioners in focusing on key variables (14).

Reinforcement learning, though less commonly applied in dentistry, is gaining traction as a method for optimizing treatment protocols. This approach involves training an algorithm to make sequential decisions that maximize a predefined reward, such as improved restoration longevity or reduced patient discomfort. Reinforcement learning models have been explored in the context of optimizing occlusal adjustments and selecting the most suitable restorative materials. By continuously learning from feedback, these models refine their predictions and adapt to diverse clinical scenarios. The integration of ML algorithms into predictive modeling not only improves accuracy but also enhances efficiency in clinical workflows. Automated systems driven by ML reduce the reliance on manual data analysis, freeing clinicians to focus on patient care. Despite these advantages, the application of ML in dentistry faces challenges such as the need for high-quality, diverse datasets and concerns about interpretability. Black-box models, such as deep learning networks, often provide predictions without clear explanations, necessitating further research to improve transparency and trust in these systems (15).

Integration of AI-Based Diagnostics with Clinical Decision-Making

The integration of AI-based diagnostic tools into clinical decision-making is reshaping how dental professionals assess and manage restorative treatments. By combining computational capabilities with clinical expertise, AI systems enhance decision-making accuracy and efficiency. These tools analyze patient data, such as imaging results, medical history, and intraoral scans, to provide actionable insights that support treatment planning. The fusion of AI diagnostics with clinical workflows ensures that patient care is optimized based on data-driven predictions and clinician judgment (5).

One of the most impactful applications of AI in diagnostics is its ability to interpret radiographic and imaging data with high precision. CNNs, for example, are widely used to identify caries, fractures, and periapical lesions in digital radiographs. These systems process imaging data in seconds, highlighting areas of concern that may be overlooked during manual evaluations. By integrating these diagnostic tools into clinical workflows, clinicians can confirm AI-generated findings with their expertise, improving diagnostic accuracy and ensuring timely interventions (16). Moreover, AI systems often provide quantitative assessments, such as bone density measurements, that aid in evaluating a patient’s suitability for implants or other restorative procedures.

AI-based diagnostics also play a crucial role in assessing restoration performance over time. Predictive models can identify early signs of failure, such as microleakages or material degradation, before they become clinically significant. For instance, time-series analysis algorithms analyze sequential data from follow-up visits to predict long-term outcomes for crowns, bridges, or implants. These capabilities allow dentists to proactively address issues, such as reinforcing weakened restorations or advising patients on improved oral hygiene practices. Additionally, AI-integrated monitoring systems can send real-time alerts to clinicians based on patient-reported outcomes, fostering a more dynamic approach to post-treatment care (17).

A key advantage of AI in clinical decision-making is its ability to synthesize data from multiple sources. By integrating patient health records, imaging results, and genetic information, AI algorithms offer a comprehensive view of a patient’s oral health. For example, Bayesian networks, a type of probabilistic model, analyze how interconnected variables—such as patient habits, material selection, and treatment protocols—influence restoration success rates. This holistic approach enables clinicians to develop personalized treatment plans tailored to individual patients’ needs, improving outcomes and minimizing risks (17). Such systems also assist in educating patients by presenting visually intuitive explanations of treatment options, fostering better communication and shared decision-making.

The introduction of AI in diagnostics is not limited to treatment planning but extends to intraoperative support. Real-time AI-powered guidance systems assist clinicians during procedures, such as crown placement or implant surgery. These systems analyze live data from intraoral cameras or sensors to provide feedback on alignment, fit, and occlusal balance. This integration enhances procedural precision, reducing the likelihood of errors and improving the functional and aesthetic quality of restorations. Additionally, AI tools enable dynamic adjustments during procedures by continuously learning from incoming data, supporting clinicians in adapting to unexpected challenges (18).

Despite its potential, the integration of AI diagnostics into clinical workflows requires careful consideration. Clinicians must be trained to interpret and validate AI-generated insights, ensuring that human expertise remains central to decision-making. Moreover, issues such as interoperability between AI systems and existing clinical software must be addressed to streamline implementation. As AI continues to evolve, its role in supporting evidence-based decision-making in dentistry will likely expand, further bridging the gap between advanced technology and clinical practice.

Challenges and Future Prospects in AI-Powered Dental Restoration Predictions

The integration of AI into dental restoration predictions, while promising, faces a range of challenges that limit its broader adoption in clinical practice. These hurdles span data availability, algorithm development, ethical considerations, and the need for interdisciplinary collaboration. Addressing these barriers is essential to fully realize AI’s potential in improving the accuracy and efficiency of dental restoration predictions (19). One of the primary challenges lies in the quality and diversity of data used to train AI models. Dental datasets are often fragmented, inconsistent, or insufficient in size to create robust predictive algorithms. Many AI models rely on retrospective data collected from various clinics or academic institutions, which can introduce biases due to differences in population demographics, equipment, and procedural techniques. Moreover, the lack of standardized protocols for data collection and annotation further complicates the development of universally applicable AI systems (20). To overcome these limitations, efforts are needed to establish centralized, well-curated repositories of dental data that encompass a wide range of patient profiles, treatment modalities, and restoration outcomes.

Another critical challenge is the interpretability of AI algorithms, particularly deep learning models. While these systems can process large datasets and generate highly accurate predictions, they often function as "black boxes," making it difficult for clinicians to understand how a specific decision was reached. This lack of transparency can lead to skepticism and hesitation in adopting AI-based tools, as clinicians may be reluctant to rely on systems they cannot fully comprehend. Developing explainable AI models, which provide clear reasoning behind predictions, is crucial for building trust and ensuring clinicians can confidently integrate these tools into their workflows (21). Ethical and legal concerns also play a significant role in hindering the adoption of AI in dentistry. Issues such as data privacy, ownership, and patient consent must be addressed to ensure that AI technologies are implemented responsibly. Many countries lack specific regulations for the use of AI in healthcare, leaving providers uncertain about compliance requirements. Furthermore, the use of patient data in AI model training raises questions about consent and data security. Clinicians and developers must work together to create frameworks that protect patient rights while enabling the use of AI to improve care. Blockchain technology has been proposed as a potential solution for ensuring secure and transparent data sharing in healthcare applications (22).

Looking to the future, interdisciplinary collaboration will be essential for advancing AI-powered dental restoration predictions. Innovations in this field require input from dentists, data scientists, material scientists, and bioengineers to ensure that algorithms are clinically relevant, technically sound, and adaptable to new materials and techniques. Advances in hardware, such as more affordable 3D scanners and real-time data processing devices, will also play a role in making AI technologies more accessible to practitioners. Additionally, incorporating AI education into dental curricula can help future clinicians develop the skills needed to effectively use these tools in their practice (23). The evolution of AI-powered dental restoration predictions also opens avenues for integrating these technologies into patient-centered care. Personalized treatment planning, enabled by AI, has the potential to optimize outcomes by tailoring interventions to individual patient needs. Future systems may also incorporate predictive analytics for preventive dentistry, identifying patients at risk of developing restoration complications before they occur. This shift toward proactive care can significantly enhance the overall quality of dental services and patient satisfaction.

Conclusion

AI and machine learning have emerged as transformative tools in predicting the success of dental restorations, enhancing diagnostic accuracy, personalization, and efficiency in clinical workflows. Despite challenges like data limitations and ethical concerns, advancements in explainable AI and interdisciplinary collaboration offer promising solutions. By addressing these barriers, AI-driven technologies can further revolutionize restorative dentistry, improving outcomes and patient care. The integration of these innovations represents a pivotal step toward the future of precision dental medicine.

Disclosure

Conflict of interest

There is no conflict of interest.

Funding

No funding.

Ethical Consideration

Not applicable.

Data availability

Data that supports the findings of this study are embedded within the manuscript.

Author Contribution

The authors contributed to conceptualizing, data drafting, collection and final writing of the manuscript.