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Qutieshat A, Al Rusheidi A, Al Ghammari S, Alarabi A, Salem A, Zelihic M. Comparative analysis of diagnostic accuracy in endodontic assessments: dental students vs. artificial intelligence. Diagnosis (Berl) 2024; 0:dx-2024-0034. [PMID: 38696271 DOI: 10.1515/dx-2024-0034] [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: 02/16/2024] [Accepted: 04/22/2024] [Indexed: 05/04/2024]
Abstract
OBJECTIVES This study evaluates the comparative diagnostic accuracy of dental students and artificial intelligence (AI), specifically a modified ChatGPT 4, in endodontic assessments related to pulpal and apical conditions. The findings are intended to offer insights into the potential role of AI in augmenting dental education. METHODS Involving 109 dental students divided into junior (54) and senior (55) groups, the study compared their diagnostic accuracy against ChatGPT's across seven clinical scenarios. Juniors had the American Association of Endodontists (AEE) terminology assistance, while seniors relied on prior knowledge. Accuracy was measured against a gold standard by experienced endodontists, using statistical analysis including Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests. RESULTS ChatGPT achieved significantly higher accuracy (99.0 %) compared to seniors (79.7 %) and juniors (77.0 %). Median accuracy was 100.0 % for ChatGPT, 85.7 % for seniors, and 82.1 % for juniors. Statistical tests indicated significant differences between ChatGPT and both student groups (p<0.001), with no notable difference between the student cohorts. CONCLUSIONS The study reveals AI's capability to outperform dental students in diagnostic accuracy regarding endodontic assessments. This underscores AIs potential as a reference tool that students could utilize to enhance their understanding and diagnostic skills. Nevertheless, the potential for overreliance on AI, which may affect the development of critical analytical and decision-making abilities, necessitates a balanced integration of AI with human expertise and clinical judgement in dental education. Future research is essential to navigate the ethical and legal frameworks for incorporating AI tools such as ChatGPT into dental education and clinical practices effectively.
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Affiliation(s)
- Abubaker Qutieshat
- Adult Restorative Dentistry, 442177 Oman Dental College , Muscat, Oman
- Restorative Dentistry, Dundee Dental Hospital and School, University of Dundee, Dundee, UK
| | | | | | | | - Abdurahman Salem
- Dental Technology, 1796 School of Health & Society, University of Bolton , Greater Manchester, UK
| | - Maja Zelihic
- Forbes School of Business and Technology, 191123 University of Arizona Global Campus , Chandler, AZ, USA
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Bokhari AM, Vinothkumar TS, Albar N, Basheer SN, Felsypremila G, Khayat WF, Zidane B, Apathsakayan R. Barriers in Rubber Dam Isolation Behaviour of Dental Students During Adhesive Restorative Treatments: A Cross-Sectional Study. Cureus 2024; 16:e58329. [PMID: 38752044 PMCID: PMC11095839 DOI: 10.7759/cureus.58329] [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] [Accepted: 04/12/2024] [Indexed: 05/18/2024] Open
Abstract
PURPOSE There are unfavorable opinions connected with rubber dam isolation amongst dental students during adhesive restorative treatments. The aim of this study was to investigate the various barriers to practicing rubber dam isolation during dental procedures and provide necessary insight towards implementation of rubber dam among undergraduate dental students in Jazan. MATERIALS AND METHODS A pre-validated questionnaire in English entitled Rubber Dam Isolation Survey (E-RDIS) based on the Capability Opportunity Motivation-Behaviour (COM-B) model of behavioral change wheel was responded by 226 university dental students. RESULTS The satisfaction of training was highest among sixth year students (Mean=3.57, p<0.001). Fourth year dental students scored higher in the capability (Mean=3.18) and were more highly motivated to use rubber dams (Mean=4.21). Third year students were more likely to use rubber dams in anterior teeth (Mean=3.52) whereas fourth year students use rubber dam in posterior teeth (Mean=3.74). Lack of motivation was found to be the significant barrier influencing rubber dam usage (odds ratio (OR)=12.1; 3.74, p<0.05). CONCLUSION The satisfaction with training differed among the students of different years. The rubber dam technique might be used more frequently if it were made clear to students that mastering it would be necessary for them to receive good grades.
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Affiliation(s)
- Ahmed M Bokhari
- Department of Preventive Dental Sciences, Division of Community Dentistry, College of Dentistry, Jazan University, Jazan, SAU
| | - Thilla Sekar Vinothkumar
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan, SAU
| | - Nassreen Albar
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan, SAU
| | - Syed Nahid Basheer
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan, SAU
| | - Gnanasekaran Felsypremila
- Department of Clinical Research, Sri Ramachandra Faculty of Clinical Research, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Waad F Khayat
- Department of Restorative Dentistry, College of Dentistry, Umm Al-Qura University, Makkah, SAU
| | - Bassam Zidane
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, SAU
| | - Renugalakshmi Apathsakayan
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan, SAU
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Adeoye J, Su YX. Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders. Int J Surg 2024; 110:1677-1686. [PMID: 38051932 PMCID: PMC10942172 DOI: 10.1097/js9.0000000000000979] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/21/2023] [Indexed: 12/07/2023]
Abstract
Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.
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Affiliation(s)
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, People’s Republic of China
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Hua D, Petrina N, Young N, Cho JG, Poon SK. Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review. Artif Intell Med 2024; 147:102698. [PMID: 38184343 DOI: 10.1016/j.artmed.2023.102698] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 09/29/2023] [Accepted: 10/29/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. METHODS A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. RESULTS The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. CONCLUSION This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.
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Affiliation(s)
- David Hua
- School of Computer Science, The University of Sydney, Australia; Sydney Law School, The University of Sydney, Australia
| | - Neysa Petrina
- School of Computer Science, The University of Sydney, Australia
| | - Noel Young
- Sydney Medical School, The University of Sydney, Australia; Lumus Imaging, Australia
| | - Jin-Gun Cho
- Sydney Medical School, The University of Sydney, Australia; Western Sydney Local Health District, Australia; Lumus Imaging, Australia
| | - Simon K Poon
- School of Computer Science, The University of Sydney, Australia; Western Sydney Local Health District, Australia.
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Putra RH, Astuti ER, Nurrachman AS, Putri DK, Ghazali AB, Pradini TA, Prabaningtyas DT. Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review. Imaging Sci Dent 2023; 53:271-281. [PMID: 38174035 PMCID: PMC10761295 DOI: 10.5624/isd.20230058] [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: 03/08/2023] [Revised: 06/14/2023] [Accepted: 07/14/2023] [Indexed: 01/05/2024] Open
Abstract
Purpose The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Material and Methods An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.
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Affiliation(s)
- Ramadhan Hardani Putra
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Eha Renwi Astuti
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Aga Satria Nurrachman
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Dina Karimah Putri
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
- Division of Dental Informatics and Radiology, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Ahmad Badruddin Ghazali
- Oral Radiology Unit, Department of Oral Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University Malaysia, Malaysia
| | - Tjio Andrinanti Pradini
- Undergraduate Program, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
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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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Sommerfeldt W, Gellert P, Müller A, Götze N, Göstemeyer G. Older patients' perception of treating root caries with silver diamine fluoride - a qualitative study based on the Theoretical Domains Framework. J Dent 2023; 130:104408. [PMID: 36626976 DOI: 10.1016/j.jdent.2022.104408] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/15/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES Caries treatment with silver diamine fluoride (SDF) is effective, but often leads to irreversible tooth discoloration. This study aimed to investigate older patients' perceptions of root caries treatment with SDF and to identify factors that influence their decision process. METHODS Fifteen interviews were conducted in older patients (mean, min/max: 83, 71/92 years) with root caries experience, following a semi-structured interview-guide based on the domains of the Theoretical Domains Framework (TDF) including three case vignettes of SDF treatment. Transcripts of the interviews were used to perform deductive and inductive content analysis along the TDF and Capability-Opportunity-Motivation-Behavior model (COM-B) to assess influential factors. RESULTS All TDF domains and behavior determinants of the COM-B were covered, identifying twenty-two barriers, facilitators and conflicting themes. Main barriers for consenting to SDF treatment were patients' perceptions of permanent staining of visible root caries lesions, as well as preconceptions about those of others and lack of knowledge about root caries and SDF. Main facilitators were trust in advice given by dentists, especially regarding new treatment options, that aesthetics were less important in non-visible areas and the importance of tooth preservation and feasibility of treatments when immobile or in need of care. CONCLUSION Permanent discoloration is an important barrier to older patients' acceptance of SDF treatment for visible root caries. However, patients appear to accept SDF treatment under certain conditions, including less visible lesions or in comparison to more invasive treatment options. CLINICAL SIGNIFICANCE Our findings contribute to understanding both barriers and facilitators when treating root caries in older patients with SDF.
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Affiliation(s)
- Wiebke Sommerfeldt
- Department of Operative, Preventive and Pediatric Dentistry, Charité Centre for Dental Medicine, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Germany
| | - Paul Gellert
- Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Anne Müller
- Department of Oral Diagnostics, Digital Health and Health Research Services, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nirina Götze
- Department of Operative, Preventive and Pediatric Dentistry, Charité Centre for Dental Medicine, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Germany
| | - Gerd Göstemeyer
- Department of Operative, Preventive and Pediatric Dentistry, Charité Centre for Dental Medicine, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Germany.
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Hemphill S, Jackson K, Bradley S, Bhartia B. The implementation of artificial intelligence in radiology: a narrative review of patient perspectives. Future Healthc J 2023; 10:63-68. [PMID: 37786489 PMCID: PMC10538685 DOI: 10.7861/fhj.2022-0097] [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] [Indexed: 04/03/2023]
Abstract
Aim To synthesise research on the view of the public and patients of the use of artificial intelligence (AI) in radiology investigations. Methods A literature review of narrative synthesis of qualitative and quantitative studies that reported views of the public and patients toward the use of AI in radiology. Results Only seven studies related to patient and public views were retrieved, suggesting that this is an underexplored area of research. Two broad themes, of confidence in the capabilities of AI, and the accountability and transparency of AI, were identified. Conclusions Both optimism and concerns were expressed by participants. Transparency in the implementation of AI, scientific validation, clear regulation and accountability were expected. Combined human and AI interpretation of imaging was strongly favoured over AI acting autonomously. The review highlights the limited engagement of the public in the adoption of AI in a radiology setting. Successful implementation of AI in this field will require demonstrating not only adequate accuracy of the technology, but also its acceptance by patients.
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Wu C, Xu H, Bai D, Chen X, Gao J, Jiang X. Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis. BMJ Open 2023; 13:e066322. [PMID: 36599634 PMCID: PMC9815015 DOI: 10.1136/bmjopen-2022-066322] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVES Medical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public's views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public's understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice. DESIGN This was a meta-synthesis of qualitative studies. METHOD A search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public's perception of the application of AI in healthcare. RESULTS Of the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public's perspective, ethical and legal concerns about medical AI from the public's perspective, and public suggestions on the application of AI in medical field. CONCLUSION Results showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public's perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice. PROSPERO REGISTRATION NUMBER CRD42022315033.
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Affiliation(s)
- Chenxi Wu
- West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, China
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Huiqiong Xu
- West China School of Nursing,Sichuan University/ Abdominal Oncology Ward, Cancer Center,West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Dingxi Bai
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Xinyu Chen
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Jing Gao
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Xiaolian Jiang
- West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol 2023; 52:20220335. [PMID: 36472627 PMCID: PMC9793453 DOI: 10.1259/dmfr.20220335] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.
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Affiliation(s)
- Kuo Feng Hung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Michael M. Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
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Thurzo A, Urbanová W, Novák B, Czako L, Siebert T, Stano P, Mareková S, Fountoulaki G, Kosnáčová H, Varga I. Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022; 10:1269. [PMID: 35885796 DOI: 10.3390/healthcare10071269] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
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Thurzo A, Jančovičová V, Hain M, Thurzo M, Novák B, Kosnáčová H, Lehotská V, Varga I, Kováč P, Moravanský N. Human Remains Identification Using Micro-CT, Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact. Molecules 2022; 27:molecules27134035. [PMID: 35807281 PMCID: PMC9268125 DOI: 10.3390/molecules27134035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Teeth, in humans, represent the most resilient tissues. However, exposure to concentrated acids might lead to their dissolving, thus making human identification difficult. Teeth often contain dental restorations from materials that are even more resilient to acid impact. This paper aims to introduce a novel method for the 3D reconstruction of dental patterns as a crucial step for the digital identification of dental records. (2) With a combination of modern methods, including micro-computed tomography, cone-beam computer tomography, and attenuated total reflection, in conjunction with Fourier transform infrared spectroscopy and artificial intelligence convolutional neural network algorithms, this paper presents a method for 3D-dental-pattern reconstruction, and human remains identification. Our research studies the morphology of teeth, bone, and dental materials (amalgam, composite, glass-ionomer cement) under different periods of exposure to 75% sulfuric acid. (3) Our results reveal a significant volume loss in bone, enamel, dentine, as well as glass-ionomer cement. The results also reveal a significant resistance by the composite and amalgam dental materials to the impact of sulfuric acid, thus serving as strong parts in the dental-pattern mosaic. This paper also probably introduces the first successful artificial intelligence application in automated-forensic-CBCT segmentation. (4) Interdisciplinary cooperation, utilizing the mentioned technologies, can solve the problem of human remains identification with a 3D reconstruction of dental patterns and their 2D projections over existing ante-mortem records.
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Affiliation(s)
- Andrej Thurzo
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia;
- Institute of Forensic Medical Expertise, Expert institute, Boženy Němcovej 8, 81104 Bratislava, Slovakia;
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81272 Bratislava, Slovakia;
- Correspondence: (A.T.); (N.M.)
| | - Viera Jančovičová
- Department of Graphic Arts Technology and Applied Photochemistry, Institute of Natural and Synthetic Polymers, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 81237 Bratislava, Slovakia;
| | - Miroslav Hain
- Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská Cesta 9, 84104 Bratislava, Slovakia;
| | - Milan Thurzo
- Department of Anthropology, Faculty of Natural Sciences, Comenius University in Bratislava, 84215 Bratislava, Slovakia;
| | - Bohuslav Novák
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia;
| | - Helena Kosnáčová
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81272 Bratislava, Slovakia;
- Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy of Sciences, Dubravska Cesta 9, 84505 Bratislava, Slovakia
| | - Viera Lehotská
- 2nd Department of Radiology, Faculty of Medicine, Comenius University in Bratislava, Heydukova 10, 81250 Bratislava, Slovakia;
| | - Ivan Varga
- Institute of Histology and Embryology, Faculty of Medicine, Comenius University in Bratislava, 81372 Bratislava, Slovakia;
| | - Peter Kováč
- Institute of Forensic Medical Expertise, Expert institute, Boženy Němcovej 8, 81104 Bratislava, Slovakia;
| | - Norbert Moravanský
- Institute of Forensic Medical Expertise, Expert institute, Boženy Němcovej 8, 81104 Bratislava, Slovakia;
- Institute of Forensic Medicine, Faculty of Medicine Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia
- Correspondence: (A.T.); (N.M.)
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Brandenburg LS, Schlager S, Harzig LS, Steybe D, Rothweiler RM, Burkhardt F, Spies BC, Georgii J, Metzger MC. A Novel Method for Digital Reconstruction of the Mucogingival Borderline in Optical Scans of Dental Plaster Casts. J Clin Med 2022; 11:2383. [PMID: 35566508 PMCID: PMC9099921 DOI: 10.3390/jcm11092383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 04/22/2022] [Accepted: 04/22/2022] [Indexed: 12/14/2022] Open
Abstract
Adequate soft-tissue dimensions have been shown to be crucial for the long-term success of dental implants. To date, there is evidence that placement of dental implants should only be conducted in an area covered with attached gingiva. Modern implant planning software does not visualize soft-tissue dimensions. This study aims to calculate the course of the mucogingival borderline (MG-BL) using statistical shape models (SSM). Visualization of the MG-BL allows the practitioner to consider the soft tissue supply during implant planning. To deploy an SSM of the MG-BL, healthy individuals were examined and the intra-oral anatomy was captured using an intra-oral scanner (IOS). The empirical anatomical data was superimposed and analyzed by principal component analysis. Using a Leave-One-Out Cross Validation (LOOCV), the prediction of the SSM was compared with the original anatomy extracted from IOS. The median error for MG-BL reconstruction was 1.06 mm (0.49–2.15 mm) and 0.81 mm (0.38–1.54 mm) for the maxilla and mandible, respectively. While this method forgoes any technical work or additional patient examination, it represents an effective and digital method for the depiction of soft-tissue dimensions. To achieve clinical applicability, a higher number of datasets has to be implemented in the SSM.
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Gnanasekaran F, Nirmal L, P S, R B, MS M, Cho VY, King NM, Anthonappa RP. Visual interpretation of panoramic radiographs in dental students using eye‐tracking technology. J Dent Educ 2022; 86:887-892. [DOI: 10.1002/jdd.12899] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 12/21/2021] [Accepted: 01/22/2022] [Indexed: 11/10/2022]
Affiliation(s)
- FelsyPremila Gnanasekaran
- Department of Central Research Facility Sri Ramachandra Faculty of Clinical Research Sri Ramachandra Institute of Higher Education and Research (Deemed to be University) Chennai India
| | - Latha Nirmal
- Department of Pediatric and Preventive Dentistry Sri Ramachandra Faculty of Dental Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed to be University) Chennai India
| | - Sujitha P
- Department of Pediatric and Preventive Dentistry SRM Kattankulathur Dental College and Hospital Chengalpattu India
| | - Bhavyaa R
- Department of Pediatric and Preventive Dentistry Sri Ramachandra Faculty of Dental Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed to be University) Chennai India
| | - Muthu MS
- Department of Pediatric and Preventive Dentistry Sri Ramachandra Faculty of Dental Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed to be University) Chennai India
- Centre of Medical and Bio‐Allied Health Sciences Research Ajman University Ajman UAE
| | - Vanessa Y Cho
- Pediatric Dentistry, Dental School The University of Western Australia Perth Australia
| | - Nigel M King
- Pediatric Dentistry, Dental School The University of Western Australia Perth Australia
| | - Robert P Anthonappa
- Pediatric Dentistry, Dental School The University of Western Australia Perth Australia
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Galsgaard A. Artificial intelligence and Multidisciplinary Team Meetings; A communication challenge for radiologists' sense of agency and position as spider in a web? Eur J Radiol 2022. [DOI: 10.1016/j.ejrad.2022.110231] [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: 10/29/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 11/19/2022]
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Chaibi A, Zaiem I. Doctor Resistance of Artificial Intelligence in Healthcare. International Journal of Healthcare Information Systems and Informatics 2022. [DOI: 10.4018/ijhisi.315618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI) has revolutionized healthcare by enhancing the quality of patient care. Despite its advantages, doctors are still reluctant to use AI in healthcare. Thus, the authors' main objective is to obtain an in-depth understanding of the barriers to doctors' adoption of AI in healthcare. The authors conducted semi-structured interviews with 11 doctors. Thematic analysis as chosen to identify patterns using QSR NVivo (version 12). The results showed that the barriers to AI adoption are lack of financial resources, need for special training, performance risk, perceived cost, technology dependency, need for human interaction, and fear of AI replacing human work.
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Affiliation(s)
- Asma Chaibi
- FSEGT, University of El Manar, Mediterranean School of Business, South Mediterranean University, Tunisia
| | - Imed Zaiem
- Faculty of Economics and Management of Nabeul, University of Carthage, Tunisia
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