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Mohammad-Rahimi H, Sohrabniya F, Ourang SA, Dianat O, Aminoshariae A, Nagendrababu V, Dummer PMH, Duncan HF, Nosrat A. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions. Int Endod J 2024; 57:1566-1595. [PMID: 39075670 DOI: 10.1111/iej.14128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 07/03/2024] [Accepted: 07/16/2024] [Indexed: 07/31/2024]
Abstract
Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.
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Affiliation(s)
- Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Fatemeh Sohrabniya
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Seyed AmirHossein Ourang
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, Maryland, USA
- Private Practice, Irvine Endodontics, Irvine, California, USA
| | - Anita Aminoshariae
- Department of Endodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | | | | | - Henry F Duncan
- Division of Restorative Dentistry, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, Maryland, USA
- Private Practice, Centreville Endodontics, Centreville, Virginia, USA
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Claman D, Sezgin E. Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models. JMIR MEDICAL EDUCATION 2024; 10:e52346. [PMID: 39331527 PMCID: PMC11451510 DOI: 10.2196/52346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 06/19/2024] [Accepted: 06/19/2024] [Indexed: 09/29/2024]
Abstract
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.
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Affiliation(s)
- Daniel Claman
- Pediatric Dentistry, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Emre Sezgin
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, United States
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, 700, Children’s Drive, Columbus, OH, 43205, United States, 1 6147223179
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Delgado-Ruiz R, Kim AS, Zhang H, Sullivan D, Awan KH, Stathopoulou PG. Generative Artificial Intelligence (Gen AI) in dental education: Opportunities, cautions, and recommendations. J Dent Educ 2024. [PMID: 39219015 DOI: 10.1002/jdd.13688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/27/2024] [Accepted: 07/20/2024] [Indexed: 09/04/2024]
Affiliation(s)
- Rafael Delgado-Ruiz
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook, New York, USA
| | - Amy S Kim
- Department of Pediatric Dentistry and Restorative Dentistry, University of Washington School of Dentistry, Seattle, Washington, USA
| | - Hai Zhang
- Department of Pediatric Dentistry and Restorative Dentistry, University of Washington School of Dentistry, Seattle, Washington, USA
| | - Diane Sullivan
- Department of Comprehensive Dentistry, University of Texas San Antonio School of Dentistry, San Antonio, Texas, USA
| | - Kamran H Awan
- Roseman University of Health Sciences College of Dental Medicine, South Jordan, Utah, USA
| | - Panagiota G Stathopoulou
- Division of Periodontology/Department of Regenerative and Reconstructive Sciences, Oregon Health & Science University School of Dentistry, Portland, Oregon, USA
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Hammoudi Halat D, Shami R, Daud A, Sami W, Soltani A, Malki A. Artificial Intelligence Readiness, Perceptions, and Educational Needs Among Dental Students: A Cross-Sectional Study. Clin Exp Dent Res 2024; 10:e925. [PMID: 38970241 PMCID: PMC11226543 DOI: 10.1002/cre2.925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 07/08/2024] Open
Abstract
OBJECTIVES With Artificial Intelligence (AI) profoundly affecting education, ensuring that students in health disciplines are ready to embrace AI is essential for their future workforce integration. This study aims to explore dental students' readiness to use AI, perceptions about AI in health education and healthcare, and their AI-related educational needs. MATERIAL AND METHODS A cross-sectional survey was conducted among dental students at the College of Dental Medicine, Qatar University. The survey assessed readiness for AI using the Medical Artificial Intelligence Readiness Scale (MAIRS). Students' perceptions of AI in healthcare and health education and their educational needs were also explored. RESULTS A total of 94 students responded to the survey. AI readiness scores were average (3.3 ± 0.64 out of 5); while participants appeared more ready for the vision and ethics domains of MAIRS, they showed less readiness regarding cognition and ability. Participants scored average on AI perceptions (3.35 ± 0.45 out of 5), with concerns regarding AI risks and disadvantages. They expressed a high need for knowledge and skills related to AI use in healthcare (84%), AI for health-related research (81.9%), and AI in radiology and imaging procedures (79.8%). Student readiness had a significant correlation with AI perceptions and perceived level of AI knowledge. CONCLUSIONS This is the first study in Qatar exploring dental students' AI readiness, perceptions, and educational needs regarding AI applications in education and healthcare. The perceived AI knowledge gaps could inform future curricular AI integration. Advancing AI skills and deepening AI comprehension can empower future dental professionals through anticipated advances in the AI-driven healthcare landscape.
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Affiliation(s)
| | - Rula Shami
- Department of Clinical Oral Health Sciences, College of Dental MedicineQU Health, Qatar UniversityDohaQatar
| | - Alaa Daud
- Department of Clinical Oral Health Sciences, College of Dental MedicineQU Health, Qatar UniversityDohaQatar
| | - Waqas Sami
- Department of Pre‐Clinical Affairs, College of NursingQU Health, Qatar UniversityDohaQatar
| | | | - Ahmed Malki
- Academic Quality DepartmentQU Health, Qatar UniversityDohaQatar
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Dascalu T, Ramezanzade S, Bakhshandeh A, Bjørndal L, Ibragimov B. AI-initiated second opinions: a framework for advanced caries treatment planning. BMC Oral Health 2024; 24:772. [PMID: 38987714 PMCID: PMC11238353 DOI: 10.1186/s12903-024-04551-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 07/01/2024] [Indexed: 07/12/2024] Open
Abstract
Integrating artificial intelligence (AI) into medical and dental applications can be challenging due to clinicians' distrust of computer predictions and the potential risks associated with erroneous outputs. We introduce the idea of using AI to trigger second opinions in cases where there is a disagreement between the clinician and the algorithm. By keeping the AI prediction hidden throughout the diagnostic process, we minimize the risks associated with distrust and erroneous predictions, relying solely on human predictions. The experiment involved 3 experienced dentists, 25 dental students, and 290 patients treated for advanced caries across 6 centers. We developed an AI model to predict pulp status following advanced caries treatment. Clinicians were asked to perform the same prediction without the assistance of the AI model. The second opinion framework was tested in a 1000-trial simulation. The average F1-score of the clinicians increased significantly from 0.586 to 0.645.
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Affiliation(s)
- Tudor Dascalu
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
| | | | - Azam Bakhshandeh
- Department of Odontology, University of Copenhagen, Copenhagen, Denmark
| | - Lars Bjørndal
- Department of Odontology, University of Copenhagen, Copenhagen, Denmark
| | - Bulat Ibragimov
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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Qamar W, Khaleeq N, Nisar A, Tariq SF, Lajber M. Exploring dental professionals' outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan. BMC Oral Health 2024; 24:542. [PMID: 38720304 PMCID: PMC11080197 DOI: 10.1186/s12903-024-04305-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
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.
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Affiliation(s)
- Wajiha Qamar
- Department of Oral Biology at Bacha Khan College of Dentistry, Mardan, Pakistan.
| | - Nadia Khaleeq
- Department of Community Dentistry, Institute of Public Health & Social Sciences, Khyber Medical University, Peshawar, Pakistan
| | - Anita Nisar
- Senior Registrar at Department of Periodontology Rehman College of Dentistry, Peshawar, Pakistan
| | - Sahibzadi Fatima Tariq
- Assistant Professor at Department of Oral Pathology Rehman College of Dentistry, Peshawar, Pakistan
| | - Mehreen Lajber
- Department of Medical Education at Bacha Khan Medical College, Mardan, Pakistan
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Bhatia AP, Lambat A, Jain T. A Comparative Analysis of Conventional and Chat-Generative Pre-trained Transformer-Assisted Teaching Methods in Undergraduate Dental Education. Cureus 2024; 16:e60006. [PMID: 38854264 PMCID: PMC11162508 DOI: 10.7759/cureus.60006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2024] [Indexed: 06/11/2024] Open
Abstract
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.
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Affiliation(s)
- Amrita P Bhatia
- Prosthodontics, Shri. Yashwantrao Chavan Memorial Medical and Rural Development Foundation's Dental College, Ahmednagar, IND
| | - Apurva Lambat
- Prosthodontics, Shri. Yashwantrao Chavan Memorial Medical and Rural Development Foundation's Dental College, Ahmednagar, IND
| | - Teerthesh Jain
- General Dentistry, Affordable Dentures and Implants, Indianapolis, USA
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8
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Aminoshariae A, Nosrat A, Nagendrababu V, Dianat O, Mohammad-Rahimi H, O'Keefe AW, Setzer FC. Artificial Intelligence in Endodontic Education. J Endod 2024; 50:562-578. [PMID: 38387793 DOI: 10.1016/j.joen.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
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.
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Affiliation(s)
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, University of Sharjah, College of Dental Medicine, Sharjah, United Arab Emirates
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | | | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Royapuram Parthasarathy P, Patil SR, Dawasaz AA, Hamid Baig FA, Karobari MI. Unlocking the Potential: Investigating Dental Practitioners' Willingness to Embrace Artificial Intelligence in Dental Practice. Cureus 2024; 16:e55107. [PMID: 38558604 PMCID: PMC10979078 DOI: 10.7759/cureus.55107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2024] [Indexed: 04/04/2024] Open
Abstract
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.
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Affiliation(s)
- Parameswari Royapuram Parthasarathy
- Centre for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, IND
| | - Santosh R Patil
- Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, IND
| | - Ali Azhar Dawasaz
- Department of Diagnostic Dental Sciences, College of Dentistry, King Khalid University, Abha, SAU
| | - Fawaz Abdul Hamid Baig
- Department of Oral and Maxillofacial Surgery, College of Dentistry, King Khalid University, Abha, SAU
| | - Mohmed Isaqali Karobari
- Dental Research Unit, Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Chennai, IND
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Kavadella A, Dias da Silva MA, Kaklamanos EG, Stamatopoulos V, Giannakopoulos K. Evaluation of ChatGPT's Real-Life Implementation in Undergraduate Dental Education: Mixed Methods Study. JMIR MEDICAL EDUCATION 2024; 10:e51344. [PMID: 38111256 PMCID: PMC10867750 DOI: 10.2196/51344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/28/2023] [Accepted: 12/11/2023] [Indexed: 12/20/2023]
Abstract
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.
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Affiliation(s)
- Argyro Kavadella
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
| | - Marco Antonio Dias da Silva
- Research Group of Teleducation and Teledentistry, Federal University of Campina Grande, Campina Grande, Brazil
| | - Eleftherios G Kaklamanos
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
- School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Vasileios Stamatopoulos
- Information Management Systems Institute, ATHENA Research and Innovation Center, Athens, Greece
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Giannakopoulos K, Kavadella A, Aaqel Salim A, Stamatopoulos V, Kaklamanos EG. Evaluation of the Performance of Generative AI Large Language Models ChatGPT, Google Bard, and Microsoft Bing Chat in Supporting Evidence-Based Dentistry: Comparative Mixed Methods Study. J Med Internet Res 2023; 25:e51580. [PMID: 38009003 PMCID: PMC10784979 DOI: 10.2196/51580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/15/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND The increasing application of generative artificial intelligence large language models (LLMs) in various fields, including dentistry, raises questions about their accuracy. OBJECTIVE This study aims to comparatively evaluate the answers provided by 4 LLMs, namely Bard (Google LLC), ChatGPT-3.5 and ChatGPT-4 (OpenAI), and Bing Chat (Microsoft Corp), to clinically relevant questions from the field of dentistry. METHODS The LLMs were queried with 20 open-type, clinical dentistry-related questions from different disciplines, developed by the respective faculty of the School of Dentistry, European University Cyprus. The LLMs' answers were graded 0 (minimum) to 10 (maximum) points against strong, traditionally collected scientific evidence, such as guidelines and consensus statements, using a rubric, as if they were examination questions posed to students, by 2 experienced faculty members. The scores were statistically compared to identify the best-performing model using the Friedman and Wilcoxon tests. Moreover, the evaluators were asked to provide a qualitative evaluation of the comprehensiveness, scientific accuracy, clarity, and relevance of the LLMs' answers. RESULTS Overall, no statistically significant difference was detected between the scores given by the 2 evaluators; therefore, an average score was computed for every LLM. Although ChatGPT-4 statistically outperformed ChatGPT-3.5 (P=.008), Bing Chat (P=.049), and Bard (P=.045), all models occasionally exhibited inaccuracies, generality, outdated content, and a lack of source references. The evaluators noted instances where the LLMs delivered irrelevant information, vague answers, or information that was not fully accurate. CONCLUSIONS This study demonstrates that although LLMs hold promising potential as an aid in the implementation of evidence-based dentistry, their current limitations can lead to potentially harmful health care decisions if not used judiciously. Therefore, these tools should not replace the dentist's critical thinking and in-depth understanding of the subject matter. Further research, clinical validation, and model improvements are necessary for these tools to be fully integrated into dental practice. Dental practitioners must be aware of the limitations of LLMs, as their imprudent use could potentially impact patient care. Regulatory measures should be established to oversee the use of these evolving technologies.
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Affiliation(s)
| | - Argyro Kavadella
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
| | - Anas Aaqel Salim
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
| | - Vassilis Stamatopoulos
- Information Management Systems Institute, ATHENA Research and Innovation Center, Athens, Greece
| | - Eleftherios G Kaklamanos
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
- School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
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12
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Singh N, Pandey A, Tikku AP, Verma P, Singh BP. Attitude, perception and barriers of dental professionals towards artificial intelligence. J Oral Biol Craniofac Res 2023; 13:584-588. [PMID: 37576799 PMCID: PMC10415790 DOI: 10.1016/j.jobcr.2023.06.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 06/27/2023] [Indexed: 08/15/2023] Open
Abstract
Aim To know attitudes, perceptions and barriers towards the use of Artificial Intelligence (AI) in dentistry in India among undergraduate and postgraduate students. Methodology A questionnaire-based cross-sectional study was conducted among participants pursuing graduation and postgraduation. The questionnaire consisted of 23 close-ended and 2 open-ended questions divided into various sections of attitude, perception and barriers. The data was analysed using Statistical Package for Social Sciences (SPSS) version 24.0. Result Out of 937 responses, 55.2% responded that they get information about AI from social media platforms. 51.3% of respondents have basic knowledge about the use of AI in dentistry. 59.6% agreed that AI can be used as a "definitive diagnostic tool" in the diagnosis of diseases. 66.5% agreed that AI can be used for radiographic diagnosis of tooth caries. 71.3% stated that AI can be used as a "treatment planning tool" in dentistry. 55.7% stated that AI should be part of undergraduate dental training. Conclusion This study concluded that both dental students are aware of the concept of AI. Participants were positive when asked if AI can increase the efficiency of diagnosis, prognosis and treatment planning procedures as well as in managing patient data. Both participants believed that the barriers to the introduction of AI in dentistry are a lack of technical resources and a lack of training personnel in college.
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Affiliation(s)
- Nishi Singh
- Department of Conservative Dentistry & Endodontics, Faculty of Dentistry, King George's Medical University (KGMU), Lucknow, UP, India
| | - Anushka Pandey
- Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Aseem Prakash Tikku
- Department of Conservative Dentistry & Endodontics, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Promila Verma
- Department of Conservative Dentistry & Endodontics, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Balendra Pratap Singh
- Department of Prosthodontics and Crown & Bridge, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
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Turkkahraman H. Embracing the Unprecedented Pace of Change: Artificial Intelligence's Impact on Dentistry and Beyond. Eur J Dent 2023; 17:567-568. [PMID: 37473781 PMCID: PMC10569829 DOI: 10.1055/s-0043-1770913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023] Open
Affiliation(s)
- Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, School of Dentistry, Indiana University, Indianapolis, Indiana, United States
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14
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Tiwari A, Kumar A, Jain S, Dhull KS, Sajjanar A, Puthenkandathil R, Paiwal K, Singh R. Implications of ChatGPT in Public Health Dentistry: A Systematic Review. Cureus 2023; 15:e40367. [PMID: 37456464 PMCID: PMC10340128 DOI: 10.7759/cureus.40367] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/10/2023] [Indexed: 07/18/2023] Open
Abstract
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.
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Affiliation(s)
- Anushree Tiwari
- Clinical Quality and Value, American Academy of Orthopaedic Surgeons, Rosemont, USA
| | - Amit Kumar
- Department of Dentistry, All India Institute of Medical Sciences, Patna, IND
| | - Shailesh Jain
- Department of Prosthodontics and Crown and Bridge, School of Dental Sciences, Sharda University, Greater Noida, IND
| | - Kanika S Dhull
- Department of Pedodontics and Preventive Dentistry, Kalinga Institute of Dental Sciences (KIIT) Deemed to be University, Bhubaneswar, IND
| | - Arunkumar Sajjanar
- Department of Pediatrics and Preventive Dentistry, Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital, Nagpur, IND
| | - Rahul Puthenkandathil
- Department of Prosthodontics and Crown and Bridge, AB Shetty Memorial Institute of Dental Sciences (ABSMIDS) Nitte (Deemed to be University), Mangalore, IND
| | - Kapil Paiwal
- Department of Oral and Maxillofacial Pathology, Daswani Dental College and Research Center, Kota, IND
| | - Ramanpal Singh
- Oral Medicine and Radiology, New Horizon Dental College and Research Institute, Bilaspur, IND
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Gardiyanoğlu E, Ünsal G, Akkaya N, Aksoy S, Orhan K. Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls. Diagnostics (Basel) 2023; 13:diagnostics13081487. [PMID: 37189586 DOI: 10.3390/diagnostics13081487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 05/17/2023] Open
Abstract
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.
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Affiliation(s)
- Emel Gardiyanoğlu
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Gürkan Ünsal
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
- DESAM Institute, Near East University, 99138 Nicosia, Cyprus
| | - Nurullah Akkaya
- Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, 99138 Nicosia, Cyprus
| | - Seçil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, 06560 Ankara, Turkey
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Strunga M, Urban R, Surovková J, Thurzo A. Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment. Healthcare (Basel) 2023; 11:healthcare11050683. [PMID: 36900687 PMCID: PMC10000479 DOI: 10.3390/healthcare11050683] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
This scoping review examines the contemporary applications of advanced artificial intelligence (AI) software in orthodontics, focusing on its potential to improve daily working protocols, but also highlighting its limitations. The aim of the review was to evaluate the accuracy and efficiency of current AI-based systems compared to conventional methods in diagnosing, assessing the progress of patients' treatment and follow-up stability. The researchers used various online databases and identified diagnostic software and dental monitoring software as the most studied software in contemporary orthodontics. The former can accurately identify anatomical landmarks used for cephalometric analysis, while the latter enables orthodontists to thoroughly monitor each patient, determine specific desired outcomes, track progress, and warn of potential changes in pre-existing pathology. However, there is limited evidence to assess the stability of treatment outcomes and relapse detection. The study concludes that AI is an effective tool for managing orthodontic treatment from diagnosis to retention, benefiting both patients and clinicians. Patients find the software easy to use and feel better cared for, while clinicians can make diagnoses more easily and assess compliance and damage to braces or aligners more quickly and frequently.
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