Smart Smile: Revolutionizing Dentistry With Artificial Intelligence

Artificial intelligence in revolutionizing orthodontic practice

This analytical research paper explores the transformative impact of artificial intelligence (AI) in orthodontics, with a focus on its objectives: Identifying current applications, evaluating benefits, addressing challenges, and projecting future developments. AI, a subset of computer science designed to simulate human intelligence, has seen rapid integration into orthodontic practice. The paper examines AI technologies such as machine learning, deep learning, natural language processing, computer vision, and robotics, which are increasingly used to analyze patient data, assist with diagnosis and treatment planning, automate routine tasks, and improve patient communication. AI systems offer precise malocclusion diagnoses, predict treatment outcomes, and customize treatment plans by leveraging dental imagery. They also streamline image analysis, improve diagnostic accuracy, and enhance patient engagement through personalized communication. The objectives include evaluating the benefits of AI in terms of efficiency, accuracy, and personalized care, while acknowledging the challenges like data quality, algorithm transparency, and practical implementation. Despite these hurdles, AI presents promising prospects in advanced imaging, predictive analytics, and clinical decision-making. In conclusion, AI holds the potential to revolutionize orthodontic practices by improving operational efficiency, diagnostic precision and patient outcomes. With collaborative efforts to overcome challenges, AI could play a pivotal role in advancing orthodontic care.

Evaluation of validity and reliability of AI Chatbots as public sources of information on dental trauma

AIM

This study aimed to assess the validity and reliability of AI chatbots, including Bing, ChatGPT 3.5, Google Gemini, and Claude AI, in addressing frequently asked questions (FAQs) related to dental trauma.

METHODOLOGY

A set of 30 FAQs was initially formulated by collecting responses from four AI chatbots. A panel comprising expert endodontists and maxillofacial surgeons then refined these to a final selection of 20 questions. Each question was entered into each chatbot three times, generating a total of 240 responses. These responses were evaluated using the Global Quality Score (GQS) on a 5-point Likert scale (5: strongly agree; 4: agree; 3: neutral; 2: disagree; 1: strongly disagree). Any disagreements in scoring were resolved through evidence-based discussions. The validity of the responses was determined by categorizing them as valid or invalid based on two thresholds: a low threshold (scores of ≥ 4 for all three responses) and a high threshold (scores of 5 for all three responses). A chi-squared test was used to compare the validity of the responses between the chatbots. Cronbach's alpha was calculated to assess the reliability by evaluating the consistency of repeated responses from each chatbot.

CONCLUSION

The results indicate that the Claude AI chatbot demonstrated superior validity and reliability compared to ChatGPT and Google Gemini, whereas Bing was found to be less reliable. These findings underscore the need for authorities to establish strict guidelines to ensure the accuracy of medical information provided by AI chatbots.

Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry

INTRODUCTION

AI is based on automated learning algorithms that use large bodies of information (big data). In the field of dentistry, AI allows the analysis of radiographs, intraoral images and other clinical recordings with unprecedented precision and speed. Facial analysis is known for helping dentists and patients achieve a satisfactory result when a restorative treatment must be realized. The objective of this study is to conduct a neural network-based computerized facial analysis using Python programming language in order to valuate its efficacy in facial point detection.

METHODS

The neural network was trained to identify the main facial and dental points: smile line, lips, size and for of the teeth, etc. A facial analysis was carried out using AI. A descriptive analysis was made with calculation of the mean and standard deviation (SD) of the precision and accuracy in each group. Analysis of variance (ANOVA) was used for the comparison of means between groups.

RESULTS

At the intersecting point between dentistry and technology, advances in artificial intelligence (AI) are producing a change in the way modern dentistry is performed. The present study evidenced lesser variability in the execution times of the neural network compared with the DSD system. This indicates that the neural network affords more consistent and predictable results, representing a significant advantage in terms of time and efficacy.

CONCLUSION

The neural network is significantly more efficient and consistent in performing facial analyses than the conventional DSD system. The neural network reduces the time needed to complete the analysis and shows lesser variability in its execution times.

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

Removal of a fractured file beyond the apical foramen using robot-assisted endodontic microsurgery: a clinical report

BACKGROUND

Endodontic file fractures are common complications of root canal treatment, and requires removal via specialized techniques such as endodontic microsurgery when the file beyond the apical foramen. It is often challenging to precisely and minimally remove a fractured file. Recently the use of dental autonomous robotic system (ATR) has shown promise in precisely and minimally in dental surgery. Therefore, this case details a technique for using the ATR system to precisely and minimally guide the removal of a fractured file beyond the apical foramen.

CASE PRESENTATION

A 48-year-old male patient, with no evidence of bone defects, was diagnosed with a file fractured completely beyond the apical foramen during root canal treatment of the right maxillary lateral incisor. Patient information was used to incorporate a digital model into preoperative planning software to develop a surgical strategy. The ATR system employs spatial alignment methods for registration, directing the robotic arm to independently locate the fractured file in accordance with the surgical plan. To maximize minimally invasive surgery, the long fractured file was removed in two stages. After removing the bone and fracture file, the clinician performed suturing under a microscope. No complications were observed during the surgery, and the treatment appeared to be successful based on the 9-month follow-up evaluation.

CONCLUSIONS

The ATR system enables precise localization of the fractured file beyond the apical foramen with intact cortical plates. This technology has the potential to improve positioning accuracy, minimize the need for invasive bone removal, reduce intraoperative time, and facilitate successful endodontic microsurgical procedures.

A Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents

Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images. Our AI-based framework enhances workflow efficiency and reduces human errors. This study is an initial step towards fully automating orthodontic diagnosis and treatment planning systems, specifically focusing on the automation of orthodontic diagnostic record classification using AI. Methods This study employed a dataset comprising 61,842 images collected from three dental clinics, distributed across 13 categories. A sequential classification approach was developed, starting with a primary model that categorized images into three main groups: extraoral, intraoral, and radiographic. Secondary models were applied within each group to perform the final classification. The proposed model, enhanced with attention modules, was trained and compared with pre-trained models such as ResNet50 (Microsoft Corporation, Redmond, Washington, United States) and InceptionV3 (Google LLC, Mountain View, California, United States). External validation was performed using 13,729 new samples to assess the artificial intelligence (AI) system's accuracy and generalizability compared to expert assessments. Results The deep learning framework achieved an accuracy of 99.24% on an external validation set, demonstrating performance almost on par with human experts. Additionally, the model demonstrated significantly faster processing times compared to manual methods. Gradient-weighted class activation mapping (Grad-CAM) visualizations confirmed that the model effectively focused on clinically relevant features during classification, further supporting its clinical applicability. Conclusion This study introduces a deep learning framework for automating the classification and archiving of orthodontic diagnostic images. The model achieved impressive accuracy and demonstrated clinically relevant feature focus through Grad-CAM visualizations. Beyond its high accuracy, the framework offers significant improvements in processing speed, making it a viable tool for real-time applications in orthodontics. This approach not only reduces the workload in healthcare settings but also lays the foundation for future automated diagnostic and treatment planning systems in digital orthodontics.

Exploring dental professionals' outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan

OBJECTIVE

The purpose of this study is to explore the perspectives, familiarity, and readiness of dental faculty members regarding the integration and application of artificial intelligence (AI) in dentistry, with a focus on the possible effects on dental education and clinical practice.

METHODOLOGY

In a mix-method cross-sectional quantitative and quantitative study conducted between June 1st and August 30th, 2023, the perspectives of faculty members from a public sector dental college in Pakistan regarding the function of AI were explored. This study used qualitative as well as quantitative techniques to analyse faculty's viewpoints on the subject. The sample size was comprised of twenty-three faculty members. The quantitative data was analysed using descriptive statistics, while the qualitative data was analysed using theme analysis.

RESULTS

Position-specific differences in faculty familiarity underscore the value of individualized instruction. Surprisingly few had ever come across AI concepts in their professional lives. Nevertheless, many acknowledged that AI had the potential to improve patient outcomes. The majority thought AI would improve dentistry education. Participants suggested a few dental specialties where AI could be useful.

CONCLUSION

The study emphasizes the significance of addressing in dental professionals' knowledge gaps about AI. The promise of AI in dentistry calls for specialized training and teamwork between academic institutions and AI developers. Graduates of dentistry programs who use AI are better prepared to navigate shifting environments. The study highlights the positive effects of AI and the value of faculty involvement in maximizing its potential for better dental education and practice.

Role of Artificial Intelligence (AI) in Patient Education and Communication in Dentistry

Effective patient education and communication are integral components of quality dental care, contributing to informed decision-making, treatment compliance, and positive clinical outcomes. However, traditional methods face challenges such as language barriers, anxiety, and information retention issues. Artificial intelligence (AI) presents innovative solutions to enhance patient engagement and communication in dentistry. This review explores the transformative role of AI in redefining patient education and communication strategies, focusing on applications, benefits, challenges, and future directions. A literature search identified articles from 2018 to 2024, encompassing empirical evidence and conceptual frameworks related to AI in dental patient engagement and communication. Key findings reveal AI's potential to offer personalized educational materials, virtual consultations, language translation tools, and virtual reality simulations, improving patient understanding and experience. Despite advancements, concerns about overreliance, accuracy, implementation costs, patient acceptance, privacy, and regulatory compliance persist. Future implications suggest AI's ability to track patient progress, analyze feedback, streamline administrative processes, and provide ongoing support, enhancing oral health outcomes. However, ethical, regulatory, and equity considerations require attention for responsible AI deployment and widespread adoption. Overall, AI holds promise for revolutionizing dental patient education, communication, and care delivery, emphasizing the need for comprehensive strategies to address emerging challenges and maximize benefits.

Unlocking the Potential: Investigating Dental Practitioners' Willingness to Embrace Artificial Intelligence in Dental Practice

BACKGROUND

Artificial intelligence (AI) holds significant promise for transforming healthcare delivery, including dentistry. However, the successful integration of AI into dental practice necessitates an understanding of dental professionals' perspectives, attitudes, and readiness to adopt AI technology. This study aimed to explore dental professionals' perceptions, attitudes, and practices regarding AI adoption in dentistry.

METHODS

This cross-sectional study was conducted among 256 dental professionals using an online questionnaire. Participants were assessed for familiarity with AI technology, perceived barriers to adoption, attitudes towards AI, current usage patterns, and factors influencing adoption decisions. Data are analysed using descriptive statistics, including frequencies, percentages, means, and standard deviations. Inferential statistics, such as chi-square tests and regression analysis, were employed to examine associations between variables and identify predictors of AI adoption in dentistry.

RESULTS

The study surveyed 256 dental professionals from various regions across India, primarily aged 30 to 50 years (mean age: 42.6), with a nearly equal gender split (male: 48.4%, female: 51.6%) and high educational attainment (67.8% with master's or doctoral degrees). Private practices were predominant (56.3%). The diagnostic algorithms and treatment planning software were well known (77.3% and 70.3% familiarity, respectively). Technical concerns (average score: 3.82 ± 0.68) were the main barriers to AI adoption, followed by financial considerations (average score: 3.45 ± 0.72), ethical and legal issues (average score: 3.21 ± 0.65), and organizational factors (average score: 3.67 ± 0.71). Despite these concerns, most participants had positive attitudes towards AI (70.3% agreed). Current usage varied, with diagnostic support and administrative tasks being the most common (44.5% and 82.8% usage, respectively). Perceived utility (average score: 4.12 ± 0.75) and ease of use (average score: 3.98 ± 0.69) significantly influenced adoption, as identified by regression analysis (perceived utility: β = 0.342, p < 0.001; ease of use: β = 0.267, p = 0.005).

CONCLUSION

This study provides valuable insights into AI adoption in dentistry, highlighting the multifaceted nature of barriers and facilitators that influence dental professionals' adoption decisions. Strategies to promote AI adoption should address practical considerations, ethical concerns, and educational needs to facilitate the integration of AI technology into dental practices.

A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment

The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.