Artificial intelligence for oral and dental healthcare: Core education curriculum

Artificial intelligence chatbots and large language models in dental education: Worldwide survey of educators

INTRODUCTION

Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education.

MATERIALS AND METHODS

A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field.

RESULTS

428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots.

CONCLUSION

A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.

Tooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study

OBJECTIVES

Accurately identification and tooth numbering on radiographs is essential for any clinicians. The aim of the present study was to validate the hypothesis that Yolov5, a type of artificial intelligence model, can be trained to detect and number teeth in periapical radiographs.

MATERIALS AND METHODS

Six thousand four hundred forty six anonymized periapical radiographs without motion-related artifacts were randomly selected from the database. All periapical radiographs in which all boundaries of any tooth could be distinguished were included in the study. The radiographic images used were randomly divided into three groups: 80% training, 10% validation, and 10% testing. The confusion matrix was used to examine model success.

RESULTS

During the test phase, 2578 labelings were performed on 644 periapical radiographs. The number of true positive was 2434 (94.4%), false positive was 115 (4.4%), and false negative was 29 (1.2%). The recall, precision, and F1 scores were 0.9882, 0.9548, and 0.9712, respectively. Moreover, the model yielded an area under curve (AUC) of 0.603 on the receiver operating characteristic curve (ROC).

CONCLUSIONS

This study showed us that YOLOv5 is nearly perfect for numbering teeth on periapical radiography. Although high success rates were achieved as a result of the study, it should not be forgotten that artificial intelligence currently only can be guides dentists for accurate and rapid diagnosis.

CLINICAL RELEVANCE

It is thought that dentists can accelerate the radiographic examination time and inexperienced dentists can reduce the error rate by using YOLOv5. Additionally, YOLOv5 can also be used in the education of dentistry students.

Trustworthy Artificial Intelligence in Dentistry: Learnings from the EU AI Act

Artificial intelligence systems (AISs) gain relevance in dentistry, encompassing diagnostics, treatment planning, patient management, and therapy. However, questions about the generalizability, fairness, and transparency of these systems remain. Regulatory and governance bodies worldwide are aiming to address these questions using various frameworks. On March 13, 2024, members of the European Parliament approved the Artificial Intelligence Act (AIA), which emphasizes trustworthiness and human-centeredness as relevant aspects to regulate AISs beyond safety and efficacy. This review presents the AIA and similar regulatory and governance efforts in other jurisdictions and lays out that regulations such as the AIA are part of a complex ecosystem of interdependent and interwoven legal requirements and standards. Current efforts to regulate dental AISs require active input from the dental community, with participation of dental research, education, providers, and patients being relevant to shape the future of dental AISs.

Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models

Unlabelled

Instructional and clinical technologies have been transforming dental education. With the emergence of artificial intelligence (AI), the opportunities of using AI in education has increased. With the recent advancement of generative AI, large language models (LLMs) and foundation models gained attention with their capabilities in natural language understanding and generation as well as combining multiple types of data, such as text, images, and audio. A common example has been ChatGPT, which is based on a powerful LLM-the GPT model. This paper discusses the potential benefits and challenges of incorporating LLMs in dental education, focusing on periodontal charting with a use case to outline capabilities of LLMs. LLMs can provide personalized feedback, generate case scenarios, and create educational content to contribute to the quality of dental education. However, challenges, limitations, and risks exist, including bias and inaccuracy in the content created, privacy and security concerns, and the risk of overreliance. With guidance and oversight, and by effectively and ethically integrating LLMs, dental education can incorporate engaging and personalized learning experiences for students toward readiness for real-life clinical practice.

Artificial Intelligence and its Awareness and Utilization among Dental Students and Private Dental Practitioners at Alkharj, Saudi Arabia

Introduction

Artificial intelligence (AI) is commonly used in the modern day medical system for medical and dental imaging diagnostics, decision support, precision, hospital monitoring, robotic assistants, and so on. All branches of dentistry have a role of AI, like endodontics, cancer diagnosis, and cephalometric analysis. With the advancing technology, dental professionals need to upgrade themselves.

Aim of the Study

To assess awareness and attitude of dental students and dental practitioners in Alkharj toward AI.

Methodology

A total of 100 dental students from a teaching institute and 100 private dental practitioners participated in the study. A closed-ended questionnaire was used containing 14 questions related to awareness and attitude toward AI. Participation was voluntary.

Results

33% of study participants were aware of the working principle of AI; 68% of study subjects are aware of uses of AI in the dental field. 87% thinks AI helps in radiological diagnosis; 56.5% thinks AI helps in cancer detection.

Conclusion

Awareness about AI among study participants was less than 50%. The overall attitude of dental professionals was positive.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Artificial intelligence: ChatGPT as a disruptive didactic strategy in dental education

PURPOSE

To evaluate the influence of ChatGPT on academic tasks performed by undergraduate dental students.

METHOD

Fifty-five participants completed scientific writing assignments. First, ChatGPT was utilized; subsequently, a conventional method involving the search of scientific articles was employed. Each task was preceded by a 30-min training session. The assignments were reviewed by professors, and an anonymous questionnaire was administered to the students regarding the usefulness of ChatGPT. Data were analyzed by Mann-Whitney U-test.

RESULTS

Final scores and scores for the criteria of utilization of evidence, evaluation of arguments, and generation of alternatives achieved higher values through the traditional method than with ChatGPT (p = 0.019, 0.042, 0.017, and <0.001, respectively). No differences were found between the two methods for the remaining criteria (p > 0.05). A total of 64.29% of the students found ChatGPT useful, 33.33% found it very useful, and 3.38% not very useful. Regarding its application in further academic activities, 54.76% considered it useful, 40.48% found it very useful, and 4.76% not very useful. A total of 61.90% of the participants indicated that ChatGPT contributed to over 25% of their productivity, while 11.9% perceived it contributed to less than 15%. Concerning the relevance of having known ChatGPT for academic tasks, 50% found it opportune, 45.24% found it very opportune, 2.38% were unsure, and the same percentage thought it is inopportune. All students provided positive feedback.

CONCLUSION

Dental students highly valued the experience of using ChatGPT for academic tasks. Nonetheless, the traditional method of searching for scientific articles yield higher scores.

Artificial Intelligence in Orthodontics: Critical Review

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

A Comparative Analysis of Conventional and Chat-Generative Pre-trained Transformer-Assisted Teaching Methods in Undergraduate Dental Education

INTRODUCTION

In the present era, individuals have the ability to improve their study organization, attendance in classes, and use of mnemonics via the utilization of contemporary technology. The use of the open AI-based application Chat Generative Pre-Trained Transformer (ChatGPT) in dentistry is a developing domain, and the integration of this technology into dental education relies on the accessibility and efficacy of AI technology, as well as the readiness of institutions to adopt it. Furthermore, it is crucial to contemplate the possible ethical ramifications associated with the utilization of AI in the field of dentistry, as well as the need for dental practitioners to have adequate training in its use. In order to include the Chat Generative Pre-Trained Transformer in the dentistry curriculum, a thorough evaluation and consultation with field specialists would be necessary. This study aimed to determine whether the Chat Generative Pre-Trained Transformer is more effective than conventional teaching methods in teaching undergraduate dental students.

METHOD

Comparative research was conducted at Shri. Yashwantrao Chavan Memorial Medical and Rural Development Foundation's Dental College, Ahmednagar. Computer-generated random numbers were used to divide 100 students into two groups. Each group consists of 50 students. A didactic lecture was given using PowerPoint (Redmond, WA: Microsoft Corp.) for both groups. Group A was given textbooks to read and Group B used the Chat Generative Pre-Trained Transformer. An online questionnaire using Google Forms (Menlo Park, CA: Google LLC), which had been pre-validated, was sent via email to both groups. The pre- and post-test scores are then compared using the t-test.

RESULT

The calculated t-value is 12.263 (at 81 degrees of freedom) and the p-value is 0.000, which is less than 0.01. Therefore, the null hypothesis is rejected, and it is concluded that conventional method scores and ChatGPT method scores for the post-test have a high significant difference. Also, it is observed that the mean scores for the conventional method are higher than the mean scores for the ChatGPT method for the post-test.

CONCLUSION

It has been concluded from the study that traditional teaching methods are more effective for learning than understanding ChatGPT.

Artificial Intelligence in Endodontic Education

AIMS

The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients.

METHODS

An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov.

RESULTS

The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization.

CONCLUSIONS

AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.

Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study

OBJECTIVES

Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry.

METHODS

Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n = 20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency.

RESULTS

Pediatric dentists were significantly more accurate (mean±SD 96.67 %± 4.3 %) than other clinicians and chatbots (p < 0.001). General dentists (88.0 % ± 6.1 %) also demonstrated significantly higher accuracy than chatbots (p < 0.001), followed by students (80.8 %±6.9 %). ChatGPT showed the highest accuracy (78 %±3 %) among chatbots. All chatbots except ChatGPT3.5 showed acceptable consistency (Cronbach alpha>0.7).

CLINICAL SIGNIFICANCE

Based on this pilot study, chatbots may be valuable adjuncts for educational purposes and for distributing information to patients. However, they are not yet ready to serve as substitutes for human clinicians in diagnostic decision-making.

CONCLUSION

In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.

Ethical principles in dental healthcare: Relevance in the current technological era of artificial intelligence

In the current technological era, dental practitioners are faced with various ethical challenges, highlighting the importance of bioethics in this healthcare discipline. The rise of artificial intelligence has recently sparked a debate regarding the privacy of patient data. While the advancements may offer innovative treatment options, their long-term effects may not be fully understood, raising questions about the responsible implementation of such methods. Thus, conscientious and ethical AI use in dentistry encompasses that patients be notified about how their data is used and also about the involvement of AI-based decision-making. This paper explores the key bioethical considerations in dental healthcare, with a focus on evidence-based AI development and use. The framework of ethical principles and guidelines provided would foster trust between the clinician and patients, while promoting the highest standards of care.

Attitudes, knowledge, and perceptions of dentists and dental students toward artificial intelligence: a systematic review

Objectives

This research was aimed at assessing comprehension, attitudes, and perspectives regarding artificial intelligence (AI) in dentistry. The null hypothesis was a lack of foundational understanding of AI in dentistry.

Methods

This systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in May 2023. The eligibility criteria included cross-sectional studies published in English until July 2023, focusing solely on dentists or dental students. Data on AI knowledge, use, and perceptions were extracted and assessed for bias risk with the Joanna Briggs Institute checklist.

Results

Of 408 publications, 22 relevant articles were identified, and 13 studies were included in the review. The average basic AI knowledge score was 58.62 % among dental students and 71.75 % among dentists. More dental students (72.01 %) than dentists (62.60 %) believed in AI's potential for advancing dentistry.

Conclusions

Thorough AI instruction in dental schools and continuing education programs for practitioners are urgently needed to maximize AI's potential benefits in dentistry. An integrated PhD program could drive revolutionary discoveries and improve patient care globally. Embracing AI with informed understanding and training will position dental professionals at the forefront of technological advancements in the field.

Perceptions and Knowledge of Undergraduate Dental Students about Artificial Intelligence in Dental Schools: A Cross-sectional Study

OBJECTIVE

This study aims to assess the perceptions and knowledge of undergraduate dental students about artificial intelligence (AI) in dental schools through a cross-sectional study.

MATERIALS AND METHODS

This was a multicenter, cross-sectional study. Participant recruitment was achieved by sending an online questionnaire to the undergraduate students at the assigned universities. The questionnaire consisted of two parts. The first seven questions record general information about participants and their perceptions of AI. The remaining questions are about the knowledge of participants about the applications of AI. The data were analyzed using SPSS version 26.

RESULTS

About 165 undergraduate students from 20 universities related to the dental sciences responded to the questionnaire. And 80.6% of participants found the use of AI in dentistry exciting. I have a basic knowledge of the working principles of AI. About 80.6% of participants believe that applications of AI should be part of undergraduate dental training. And 66.6% of students are aware of the opportunities and threats that AI can create. The results show that 75% of the students indicated that they got their information about AI through social media. Regarding the association of years of studies with AI applications used in periodontics, the knowledge about AI applications in "aggressive periodontics," "compromised teeth," and "success in rate of dental implant" was significantly higher in senior students than junior students (p < 0.05). Concerning applications of AI used in restorative dentistry and prosthodontics, only "computer color matching," "tooth surface losses," and "I do not know" showed statistical significance (p < 0.05) with the year of study of participants. Senior students show significantly better knowledge in "success in retreatment" and "working length determinant."

CONCLUSION

Although undergraduates are enthusiastic about AI and aware of its threats and benefits, their knowledge is limited. In addition, undergraduate programs must exert more effort to prepare students for the era of AI. How to cite this article: Aldowah O, Almakrami A, Alghuwaynim Y, et al. Perceptions and Knowledge of Undergraduate Dental Students about Artificial Intelligence in Dental Schools: A Cross-sectional Study. J Contemp Dent Pract 2024;25(2):148-155.

Evaluation of ChatGPT's Real-Life Implementation in Undergraduate Dental Education: Mixed Methods Study

BACKGROUND

The recent artificial intelligence tool ChatGPT seems to offer a range of benefits in academic education while also raising concerns. Relevant literature encompasses issues of plagiarism and academic dishonesty, as well as pedagogy and educational affordances; yet, no real-life implementation of ChatGPT in the educational process has been reported to our knowledge so far.

OBJECTIVE

This mixed methods study aimed to evaluate the implementation of ChatGPT in the educational process, both quantitatively and qualitatively.

METHODS

In March 2023, a total of 77 second-year dental students of the European University Cyprus were divided into 2 groups and asked to compose a learning assignment on "Radiation Biology and Radiation Protection in the Dental Office," working collaboratively in small subgroups, as part of the educational semester program of the Dentomaxillofacial Radiology module. Careful planning ensured a seamless integration of ChatGPT, addressing potential challenges. One group searched the internet for scientific resources to perform the task and the other group used ChatGPT for this purpose. Both groups developed a PowerPoint (Microsoft Corp) presentation based on their research and presented it in class. The ChatGPT group students additionally registered all interactions with the language model during the prompting process and evaluated the final outcome; they also answered an open-ended evaluation questionnaire, including questions on their learning experience. Finally, all students undertook a knowledge examination on the topic, and the grades between the 2 groups were compared statistically, whereas the free-text comments of the questionnaires were thematically analyzed.

RESULTS

Out of the 77 students, 39 were assigned to the ChatGPT group and 38 to the literature research group. Seventy students undertook the multiple choice question knowledge examination, and examination grades ranged from 5 to 10 on the 0-10 grading scale. The Mann-Whitney U test showed that students of the ChatGPT group performed significantly better (P=.045) than students of the literature research group. The evaluation questionnaires revealed the benefits (human-like interface, immediate response, and wide knowledge base), the limitations (need for rephrasing the prompts to get a relevant answer, general content, false citations, and incapability to provide images or videos), and the prospects (in education, clinical practice, continuing education, and research) of ChatGPT.

CONCLUSIONS

Students using ChatGPT for their learning assignments performed significantly better in the knowledge examination than their fellow students who used the literature research methodology. Students adapted quickly to the technological environment of the language model, recognized its opportunities and limitations, and used it creatively and efficiently. Implications for practice: the study underscores the adaptability of students to technological innovations including ChatGPT and its potential to enhance educational outcomes. Educators should consider integrating ChatGPT into curriculum design; awareness programs are warranted to educate both students and educators about the limitations of ChatGPT, encouraging critical engagement and responsible use.

Implications of large language models such as ChatGPT for dental medicine

OBJECTIVE

This article provides an overview of the implications of ChatGPT and other large language models (LLMs) for dental medicine.

OVERVIEW

ChatGPT, a LLM trained on massive amounts of textual data, is adept at fulfilling various language-related tasks. Despite its impressive capabilities, ChatGPT has serious limitations, such as occasionally giving incorrect answers, producing nonsensical content, and presenting misinformation as fact. Dental practitioners, assistants, and hygienists are not likely to be significantly impacted by LLMs. However, LLMs could affect the work of administrative personnel and the provision of dental telemedicine. LLMs offer potential for clinical decision support, text summarization, efficient writing, and multilingual communication. As more people seek health information from LLMs, it is crucial to safeguard against inaccurate, outdated, and biased responses to health-related queries. LLMs pose challenges for patient data confidentiality and cybersecurity that must be tackled. In dental education, LLMs present fewer challenges than in other academic fields. LLMs can enhance academic writing fluency, but acceptable usage boundaries in science need to be established.

CONCLUSIONS

While LLMs such as ChatGPT may have various useful applications in dental medicine, they come with risks of malicious use and serious limitations, including the potential for misinformation.

CLINICAL SIGNIFICANCE

Along with the potential benefits of using LLMs as an additional tool in dental medicine, it is crucial to carefully consider the limitations and potential risks inherent in such artificial intelligence technologies.

The challenges imposed by artificial intelligence: are we ready in medical education?

Artificial intelligence (AI) is the science and engineering of making intelligent machines. In medical education, the usefulness of AI and its applications is being explored in training, learning, simulation, curriculum, and developing new assessment tools. This editorial encourages authors to submit their research on AI concerning medical education to enrich our knowledge.

Implications of ChatGPT in Public Health Dentistry: A Systematic Review

An artificial intelligence (AI) program called ChatGPT that generates text in response to typed commands has proven to be highly popular, as evidenced by the fact that OpenAI makes it available online. The goal of the present investigation was to investigate ChatGPT's potential applications as an outstanding instance of large language models (LLMs) in the fields of public dental health schooling, writing for academic use, research in public dental health, and clinical practice in public dental health based on the available data. Importantly, the goals of the current review included locating any drawbacks and issues that might be connected to using ChatGPT in the previously mentioned contexts in healthcare settings. Using search phrases including chatGPT, implications, artificial intelligence (AI), public health dentistry, public health, practice in public health dentistry, education in public health dentistry, academic writing in public health dentistry, etc., a thorough search was carried out on the Pubmed database, the Embase database, the Ovid database, the Global Health database, PsycINFO, and the Web of Science. The dates of publication were not restricted. Systematic searches were carried out for all publications according to inclusion and exclusion criteria between March 31, 2018, and March 31, 2023. Eighty-four papers were obtained through a literature search using search terms. Sixteen similar and duplicate papers were excluded and 68 distinct articles were initially selected. Thirty-three articles were excluded after reviewing abstracts and titles. Thirty-five papers were selected, for which full text was managed. Four extra papers were found manually from references. Thirty-nine articles with full texts were eligible for the study. Eighteen inadequate articles are excluded from the final 21 studies that were finally selected for systemic review. According to previously published studies, ChatGPT has demonstrated its effectiveness in helping scholars with the authoring of scientific research and dental studies. If the right structures are created, ChatGPT can offer suitable responses and more time to concentrate on the phase of experimentation for scientists. Risks include prejudice in the training data, undervaluing human skills, the possibility of fraud in science, as well as legal and reproducibility concerns. It was concluded that practice considering ChatGPT's potential significance, the research's uniqueness, and the premise-the activity of the human brain-remains. While there is no question about the superiority of incorporating ChatGPT into the practice of public health dentistry, it does not, in any way, take the place of a dentist since clinical practice involves more than just making diagnoses; it also involves relating to clinical findings and providing individualized patient care. Even though AI can be useful in a number of ways, a dentist must ultimately make the decision because dentistry is a field that involves several disciplines.

Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

BACKGROUND

The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs).

METHODS

8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique.

RESULTS

The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively.

CONCLUSIONS

Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.