Application of artificial intelligence in the dental field : A literature review

Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types. Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning

Sustaining Dental Practices Longer Than 5 Years

Dentists graduate dental school ready to practice dentistry, but 85% do not feel prepared by the dental school to open and manage the operations of a general dental practice. General systems theory grounded this multisite case study. The research provides information on 3 solo practitioner dental practices that sustained beyond 5 years in the Washington, DC suburbs. At each operating practice, the dentist who owned the practice and 1 employee that also worked at the practice during the first 5 years were interviewed. The dentist provided marketing documents used during the first 5 years of the practice operations. Data triangulation was used to ensure the trustworthiness of the analysis of the data from the interviews and documents collected. The data collected was analyzed using coding, establishing nodes, and creating mind maps to identify 5 themes. The themes included working hard to provide dental care and relieve pain, marketing to ensure potential patients had the practice contact information when they needed it, learning continuously to improve the practice operations, putting patient\u27s health before practice profits, and minimizing debt. The implications for positive social change for residents of the Washington, DC suburbs include the potential to receive the needed dental care and pain relief they need because dentists who learn from this research will stay late and return to their practice to treat patients who found the dentist\u27s contact information from their marketing. The implications for positive social change for owners of dental practices include building a sustainable dental practice by implementing these research findings that include working hard, marketing, continuous learning, putting patients health first, and minimizing debt

Unsupervised learning for anomaly detection in Australian medical payment data

Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia’s universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of$A1–2.7 billion could be expected.However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks. Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available. In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this fiel

Private Sector Defense Contractor Management Strategies for Contract Fulfillment

Private sector U.S. defense contractors failing to meet contract objectives experience lower profitability, pay costly penalties, and risk survivability. Using the theory of contracts, the purpose of this multiple case study was to the explore strategies that some leaders of U.S. defense contracting businesses use to meet all the contract terms with the U.S. Department of Defense. Data were collected from 5 leaders of private sector defense contracting companies in northwest Florida through face-to-face, semistructured interviews and through a review of operations manuals, quality assurance policy manuals, and archived U.S. Department of Defense contracts. During data analysis using Yin\u27s 5-step process of compiling, disassembling, reassembling, interpreting, and concluding the data, 4 themes on strategies emerged: (a) communication strategy for successful completion of contracts, (b) technology strategy to monitor contract compliance, (c) training strategy for program managers, and (d) subcontractor selection strategy. The findings indicated that communication among all contract parties was an essential component of each of the 4 themes. Computerizing the contracting workflow to monitor compliance efforts, training program managers for effective oversight of contract compliance, and selecting subcontractors were vital elements of the strategies private sector defense contractors used to meet all the terms and conditions of U.S. Department of Defense contracts. The implications for positive social change include the potential for private sector defense contractors to improve the strength of the defense of the northwest Florida community, lower unemployment, and provide a safer environment for humanity

Oral Health Care: An Autoethnography Reflecting on Dentistry\u27s Collective Neglect and Changes in Professional Education Resulting in the Dental Hygienist Being the Prevention-focused Primary Oral Health Care Provider

Many factors influence poor oral health among disadvantaged populations including socioeconomic circumstances, knowledge of disease prevention strategies and ability to implement those strategies, public policies, insurance status, insurance policies, dental providers and other challenges to accessing dental care. Often these issues converge and result in early disadvantages to achieving good oral health (Horton & Barker, 2010). Addressing even some of the factors that contribute to poor oral health may provide ways to change the dental health status of historically underserved populations. The purpose of this research is to explore my role as a practitioner and researcher in the creation of a hygienist-based, community-site located, teledentistry supported system of dental care for underserved populations and the intersection of my experiences with cultural, societal and educational occurrences. This autoethnography examined my own experiences and also explored the experiences of a small sample of others who participated in onsite dental care systems utilizing hygienists as the prevention-focused primary care provider. As Ellis and Bochner (1996) note “Autoethnography stands as a current attempt to, quite literally, come to terms with sustaining questions of self and culture” (p. 193). The findings that emerged from my work included a realization that the dental industry creates and perpetuates the collective neglect of large portions of the US population. Some of this neglect is embedded in traditional power structures in dentistry, gender bias and distrust in professional skills as a result of separate professional education structures. The result for many people is untreated dental disease, a profound lack of health equity, increased shame due to poor oral health as well as missing school. There are ways to address the collective neglect of the dental industry through the reframing of the dental hygienist as the prevention-focused primary care oral health provider in professional education programs then integrating this provider type into community settings like schools

Characteristics of Medical Research News Reported on Front Pages of Newspapers

Background: The placement of medical research news on a newspaper's front page is intended to gain the public's attention, so it is important to understand the source of the news in terms of research maturity and evidence level. Methodology/Principal Findings: We searched LexisNexis to identify medical research reported on front pages of major newspapers published from January 1, 2000 to December 31, 2002. We used MEDLINE and Google Scholar to find journal articles corresponding to the research, and determined their evidence level. Of 734 front-page medical research stories identified, 417 (57%) referred to mature research published in peer-reviewed journals. The remaining 317 stories referred to preliminary findings presented at scientific or press meetings; 144 (45%) of those stories mentioned studies that later matured (i.e. were published in journals within 3 years after news coverage). The evidence-level distribution of the 515 journal articles quoted in news stories reporting on mature research (3% level I, 21% level II, 42% level III, 4% level IV, and 31% level V) differed from that of the 170 reports of preliminary research that later matured (1%, 19%, 35%, 12%, and 33%, respectively; chi-square test, P = .0009). No news stories indicated evidence level. Fewer than 1 in 5 news stories reporting preliminary findings acknowledged the preliminary nature of their content. Conclusions/Significance: Only 57% of front-page stories reporting on medical research are based on mature research, which tends to have a higher evidence level than research with preliminary findings. Medical research news should be clearly referenced and state the evidence level and limitations to inform the public of the maturity and quality of the source. © 2009 Lai, Lane.published_or_final_versio

Characteristics of Medical Research News Reported on Front Pages of Newspapers

Background: The placement of medical research news on a newspaper's front page is intended to gain the public's attention, so it is important to understand the source of the news in terms of research maturity and evidence level. Methodology/Principal Findings: We searched LexisNexis to identify medical research reported on front pages of major newspapers published from January 1, 2000 to December 31, 2002. We used MEDLINE and Google Scholar to find journal articles corresponding to the research, and determined their evidence level. Of 734 front-page medical research stories identified, 417 (57%) referred to mature research published in peer-reviewed journals. The remaining 317 stories referred to preliminary findings presented at scientific or press meetings; 144 (45%) of those stories mentioned studies that later matured (i.e. were published in journals within 3 years after news coverage). The evidence-level distribution of the 515 journal articles quoted in news stories reporting on mature research (3% level I, 21% level II, 42% level III, 4% level IV, and 31% level V) differed from that of the 170 reports of preliminary research that later matured (1%, 19%, 35%, 12%, and 33%, respectively; chi-square test, P = .0009). No news stories indicated evidence level. Fewer than 1 in 5 news stories reporting preliminary findings acknowledged the preliminary nature of their content. Conclusions/Significance: Only 57% of front-page stories reporting on medical research are based on mature research, which tends to have a higher evidence level than research with preliminary findings. Medical research news should be clearly referenced and state the evidence level and limitations to inform the public of the maturity and quality of the source. © 2009 Lai, Lane.published_or_final_versio