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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Al Hendi KD, Alyami MH, Alkahtany M, Dwivedi A, Alsaqour HG. Artificial intelligence in prosthodontics. Bioinformation 2024; 20:238-242. [PMID: 38712003 PMCID: PMC11069608 DOI: 10.6026/973206300200238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/31/2024] [Accepted: 03/31/2024] [Indexed: 05/08/2024] Open
Abstract
Artificial Intelligence (AI) is gaining popularity worldwide owing to its significant impact in science and innovation. The role of AI in prosthodontics has increased significantly in recent years. AI is used for diagnosis, decision-making, prognosis, treatment planning and prediction of outcomes. Integration of AI into prosthodontics can enhance the accuracy and precision of dental practice. However, limited datasets are a major constraint in its practical applications.
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Affiliation(s)
| | | | - Mashary Alkahtany
- Prosthetic Dental Sciences, Faculty of Dentistry, Najran University, Najran, KSA
| | - Alok Dwivedi
- Prosthetic Dental Sciences, Faculty of Dentistry, Najran University, Najran, KSA
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Chakravorty S, Aulakh BK, Shil M, Nepale M, Puthenkandathil R, Syed W. Role of Artificial Intelligence (AI) in Dentistry: A Literature Review. J Pharm Bioallied Sci 2024; 16:S14-S16. [PMID: 38595379 PMCID: PMC11001144 DOI: 10.4103/jpbs.jpbs_466_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/05/2023] [Accepted: 08/07/2023] [Indexed: 04/11/2024] Open
Abstract
Artificial intelligence (AI) refers to the ability of machines to execute tasks that traditionally require human intelligence. Healthcare AI is evolving and has a highly promising future. The main uses in dentistry are patient management, diagnosis and treatment planning, and administrative tasks. Consequently, this AI system facilitates the acquisition of familiarity with this technology for every dentist, as it possesses the potential to synergistically complement forthcoming revolutionary advancements in the field of dentistry. This review aims to explore and analyze the several applications of AI within the field of dentistry.
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Affiliation(s)
- Shibani Chakravorty
- Department of Conservative Dentistry and Endodontics, Kalinga Institute of Dental Sciences, KIIT University, Bhubaneswar, Odisha, India
| | - Basant Kaur Aulakh
- Department of Periodontology, Himachal Dental College, Sundernagar, Himachal Pradesh, India
| | - Malabika Shil
- Oral and Maxillofacial Radiologist, Nidaan Diagnostic Centre, Pune, Maharashtra, India
| | - Mayuri Nepale
- Regional Medical Advisor, Abbott, Mumbai, Maharashtra, India
| | - Rahul Puthenkandathil
- Department of Prosthodontics and Crown and Bridge, Nitte (Deemed to be University), AB Shetty Memorial Institute of Dental Sciences (ABSMIDS), Mangalore, Karnataka, India
| | - Wali Syed
- Department of Conservative Dentistry and Endodontics, Panineeya Institute of Dental Sciences and Research Centre, Hyderabad, Telangana, India
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Ying S, Huang F, Liu W, He F. Deep learning in the overall process of implant prosthodontics: A state-of-the-art review. Clin Implant Dent Relat Res 2024. [PMID: 38286659 DOI: 10.1111/cid.13307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/31/2024]
Abstract
Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.
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Affiliation(s)
- Shunv Ying
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Feng Huang
- School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Wei Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Fuming He
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
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Asgary S. Artificial Intelligence in Endodontics: A Scoping Review. Iran Endod J 2024; 19:85-98. [PMID: 38577001 PMCID: PMC10988643 DOI: 10.22037/iej.v19i2.44842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/23/2024] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Artificial intelligence (AI) is transforming the diagnostic methods and treatment approaches in the constantly evolving field of endodontics. The current review discusses the recent advancements in AI; with a specific focus on convolutional and artificial neural networks. Apparently, AI models have proved to be highly beneficial in the analysis of root canal anatomy, detecting periapical lesions in early stages as well as providing accurate working-length determination. Moreover, they seem to be effective in predicting the treatment success next to identifying various conditions e.g., dental caries, pulpal inflammation, vertical root fractures, and expression of second opinions for non-surgical root canal treatments. Furthermore, AI has demonstrated an exceptional ability to recognize landmarks and lesions in cone-beam computed tomography scans with consistently high precision rates. While AI has significantly promoted the accuracy and efficiency of endodontic procedures, it is of high importance to continue validating the reliability and practicality of AI for possible widespread integration into daily clinical practice. Additionally, ethical considerations related to patient privacy, data security, and potential bias should be carefully examined to ensure the ethical and responsible implementation of AI in endodontics.
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Affiliation(s)
- Saeed Asgary
- Iranian Center for Endodontic Research, Research Institute for Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Affiliation(s)
- Girish Malleshappa Sogi
- Editor-in-chief, Contemporary Clinical Dentistry, Principal cum Dean, MM College of Dental Sciences and Research, Maharishi Markandeshwar (Deemed to be University), Ambala, Haryana, India E-mail:
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Mahesh Batra A, Reche A. A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment. Cureus 2023; 15:e49319. [PMID: 38143639 PMCID: PMC10748804 DOI: 10.7759/cureus.49319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 11/23/2023] [Indexed: 12/26/2023] Open
Abstract
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.
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Affiliation(s)
- Aastha Mahesh Batra
- Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Amit Reche
- Public Health Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Chawla RL, Gadge NP, Ronad S, Waghmare A, Patil A, Deshmukh G. Knowledge, Attitude and Perception Regarding Artificial Intelligence in Periodontology: A Questionnaire Study. Cureus 2023; 15:e48309. [PMID: 38058340 PMCID: PMC10697475 DOI: 10.7759/cureus.48309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2023] [Indexed: 12/08/2023] Open
Abstract
INTRODUCTION The utilization of artificial intelligence (AI) and machine learning (ML) models has brought about a significant transformation in the manner in which periodontists gather information, evaluate associated risks, develop diverse treatment alternatives, anticipate and diagnose dental conditions that compromise periodontal health. The principal objective of this prospective study was to examine periodontists' understanding and acceptance of the application of AI in the realm of periodontology. MATERIALS AND METHODS This observational study was conducted on 275 participants based on questionnaire using Google Forms. These forms were pre-validated and subsequently circulated among periodontists in Maharashtra via various social media platforms. The study, in its entirety, comprised four open-ended questions on participants' demographics and 14 closed-ended questions, all of which were presented to the participants in English. These questions aimed to elicit participants' awareness, knowledge, attitudes, and perspectives regarding emerging applications of AI in the field of periodontology. To analyze the collected data, researchers employed the widely utilized Statistical Package for Social Sciences (SPSS) version 22.0. RESULT A 75% response rate was achieved and 68% of the respondents were female. 62% periodontists were aware of AI; however, only 24% were aware of its working principles. Most respondents agreed with the use of AI in periodontal diagnosis; however, they disagreed with the use of AI in predicting clinical attachment loss (69%). 80-82% respondents felt that AI should be a part of postgraduate training and should be implemented in clinical practice. However, most periodontists do not use AI for diagnostic or research purposes. 49% periodontists felt that AI does not have better diagnostic accuracy than periodontists, and therefore cannot replace them in the future. CONCLUSION Most periodontists possessed a reasonable level of understanding regarding the utilization of AI in the domain of periodontology and expressed a desire to incorporate it into their diagnostic and treatment planning processes for periodontal conditions. Additional endeavors must be undertaken to enhance periodontists' awareness concerning the effective implementation of AI within their professional practice, with the aim of facilitating personalized treatment planning for their respective patients. It is postulated that the integration of AI will augment the likelihood of achieving favorable outcomes within the realm of periodontology.
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Affiliation(s)
- Ruhee L Chawla
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Nidhi P Gadge
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Sunil Ronad
- Prosthodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Alka Waghmare
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Aarti Patil
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
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Vodanović M, Subašić M, Milošević DP, Galić I, Brkić H. Artificial intelligence in forensic medicine and forensic dentistry. J Forensic Odontostomatol 2023; 41:30-41. [PMID: 37634174 PMCID: PMC10473456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.
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Affiliation(s)
- M Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
| | - M Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - D P Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - I Galić
- School of Medicine, University of Split, Croatia
| | - H Brkić
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
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Abstract
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
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Affiliation(s)
| | - Priyanka Porwal
- Dentistry, Pushpagiri Institute of Medical Sciences and Research Centre, Tiruvalla, IND
| | - Amit Porwal
- Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan, SAU
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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: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Abesi F, Maleki M, Zamani M. Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis. Imaging Sci Dent 2023; 53:101-108. [PMID: 37405196 PMCID: PMC10315225 DOI: 10.5624/isd.20220224] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 04/12/2024] Open
Abstract
PURPOSE The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans. MATERIALS AND METHODS A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals (CIs). RESULTS In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation. CONCLUSION Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.
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Affiliation(s)
- Farida Abesi
- Department of Oral and Maxillofacial Radiology, Dental Faculty, Babol University of Medical Sciences, Babol, Iran
| | - Mahla Maleki
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Mohammad Zamani
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
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Abstract
Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence chatbot that uses natural language processing that can respond to human input in a conversational manner. ChatGPT has numerous applications in the health care system including dentistry; it is used in diagnoses and for assessing disease risk and scheduling appointments. It also has a role in scientific research. In the dental field, it has provided many benefits such as detecting dental and maxillofacial abnormalities on panoramic radiographs and identifying different dental restorations. Therefore, it helps in decreasing the workload. But even with these benefits, one should take into consideration the risks and limitations of this chatbot. Few articles mentioned the use of ChatGPT in dentistry. This comprehensive review represents data collected from 66 relevant articles using PubMed and Google Scholar as databases. This review aims to discuss all relevant published articles on the use of ChatGPT in dentistry.
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Affiliation(s)
- Hind M Alhaidry
- Advanced General Dentistry, Prince Sultan Military Medical City, Riyadh, SAU
| | - Bader Fatani
- Dentistry, College of Dentistry, King Saud University, Riyadh, SAU
| | - Jenan O Alrayes
- Dentistry, College of Dentistry, King Saud University, Riyadh, SAU
| | | | - Nawaf K Alfhaed
- Dentistry, College of Dentistry, King Saud University, Riyadh, SAU
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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Hsu K, Yuh DY, Lin SC, Lyu PS, Pan GX, Zhuang YC, Chang CC, Peng HH, Lee TY, Juan CH, Juan CE, Liu YJ, Juan CJ. Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography. Sci Rep 2022; 12:19809. [PMID: 36396696 PMCID: PMC9672125 DOI: 10.1038/s41598-022-23901-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022] Open
Abstract
Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal-Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.
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Affiliation(s)
- Kang Hsu
- grid.260565.20000 0004 0634 0356Department of Periodontology, School of Dentistry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC ,grid.260565.20000 0004 0634 0356School of Dentistry and Graduate Institute of Dental Science, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Da-Yo Yuh
- grid.260565.20000 0004 0634 0356Department of Periodontology, School of Dentistry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Shao-Chieh Lin
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Ph.D. Program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, ROC
| | - Pin-Sian Lyu
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724 Taichung Taiwan, ROC
| | - Guan-Xin Pan
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC
| | - Yi-Chun Zhuang
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC
| | - Chia-Ching Chang
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.260539.b0000 0001 2059 7017Department of Management Science, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Hsu-Hsia Peng
- grid.38348.340000 0004 0532 0580Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Tung-Yang Lee
- grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC ,grid.413844.e0000 0004 0638 8798Cheng Ching Hospital, Taichung, Taiwan, ROC
| | - Cheng-Hsuan Juan
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC ,grid.413844.e0000 0004 0638 8798Cheng Ching Hospital, Taichung, Taiwan, ROC
| | - Cheng-En Juan
- grid.411298.70000 0001 2175 4846Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724 Taichung Taiwan, ROC
| | - Yi-Jui Liu
- grid.411298.70000 0001 2175 4846Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724 Taichung Taiwan, ROC
| | - Chun-Jung Juan
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.38348.340000 0004 0532 0580Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC ,grid.254145.30000 0001 0083 6092Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, ROC ,grid.411508.90000 0004 0572 9415Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan, ROC ,grid.260565.20000 0004 0634 0356Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan, ROC ,grid.19188.390000 0004 0546 0241Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC
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Singi SR, Sathe S, Reche AR, Sibal A, Mantri N. Extended Arm of Precision in Prosthodontics: Artificial Intelligence. Cureus 2022; 14:e30962. [DOI: 10.7759/cureus.30962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
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Ajami M, Tripathi P, Ling H, Mahdian M. Automated Detection of Cervical Carotid Artery Calcifications in Cone Beam Computed Tomographic Images Using Deep Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12102537. [PMID: 36292226 PMCID: PMC9600983 DOI: 10.3390/diagnostics12102537] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of this study was to determine if a convolutional neural network (CNN) can be trained to automatically detect and localize cervical carotid artery calcifications (CACs) in CBCT. A total of 56 CBCT studies (15,257 axial slices) were utilized to train, validate, and test the deep learning model. The study comprised of two steps: Step 1: Localizing axial slices that are below the C2–C3 disc space. For this step the openly available Inception V3 architecture was trained on the ImageNet dataset of real-world images, and retrained on 40 CBCT studies. Step 2: Detecting CACs in slices from step 1. For this step, two methods were implemented; Method A: Segmentation neural network trained using small patches at random coordinates of the original axial slices; Method B: Segmentation neural network trained using two larger patches at fixed coordinates of the original axial slices with an improved loss function to account for class imbalance. Our approach resulted in 94.2% sensitivity and 96.5% specificity. The mean intersection over union metric for Method A was 76.26% and Method B improved this metric to 82.51%. The proposed CNN model shows the feasibility of deep learning in the detection and localization of CAC in CBCT images.
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Affiliation(s)
- Maryam Ajami
- School of Dental Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Pavani Tripathi
- Department of Computer Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Haibin Ling
- Department of Computer Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, School of Dental Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Correspondence:
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