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Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review.

ABSTRACT: Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.

SUBMITTER: Umer F

PROVIDER: S-EPMC10860267 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review.

Umer Fahad F Adnan Samira S Lal Abhishek A

BMC oral health 20240212 1

Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three ...[more]

PMID: 38347508

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Project description:BackgroundImprovements in health-related outcomes for critically ill adults in low and lower-middle income countries need systematic investments in research capacity and infrastructure. High-quality research has been shown to strengthen health systems; yet, research contributions from these regions remain negligible or absent. We undertook a scoping review to describe barriers and facilitators for the conduct of critical care research.MethodsWe searched MEDLINE and EMBASE up to December 2021 using a strategy that combined keyword and controlled vocabulary terms. We included original studies that reported on barriers or facilitators to the conduct of critical care research in these settings. Two reviewers independently reviewed titles and abstracts, and where necessary, the full-text to select eligible studies. For each study, reviewers independently extracted data using a standardized data extraction form. Barriers and facilitators were classified along the lines of a previous review and based on additional themes that emerged. Study quality was assessed using appropriate tools.ResultsWe identified 2693 citations, evaluated 49 studies and identified 6 for inclusion. Of the included studies, four were qualitative, one was a cross-sectional survey and one was reported as an 'analysis'. The total number of participants ranged from 20-100 and included physicians, nurses, allied healthcare workers and researchers. Barriers identified included limited funding, poor institutional & national investment, inadequate access to mentors, absence of training in research methods, limited research support staff, and absence of statistical support. Our review identified potential solutions such as developing a mentorship network, streamlining of regulatory processes, implementing a centralized institutional research agenda, developing a core-outcome dataset and enhancing access to low-cost technology.ConclusionOur scoping review highlights important barriers to the conduct of critical care research in low and lower-middle income countries, identifies potential solutions, and informs researchers, policymakers and governments on the steps necessary for strengthening research systems.

Project description:The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

Dataset Information

Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review.

Publications

Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review.

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