METHODS AND ANALYSIS: A three-phase approach to validate content for curriculum guidelines on AMR will be adopted. First, literature review and content analysis were conducted to find out the available pertinent literature in dentistry programmes. A total of 23 potential literature have been chosen for inclusion within this study following literature review and analysis in phase 1. The materials found will be used to draft curriculum on antimicrobials for dentistry programmes. The next phase involves the validation of the drafted curriculum content by recruiting local and foreign experts via a survey questionnaire. Finally, Delphi technique will be conducted to obtain consensus on the important or controversial modifications to the revised curriculum.
ETHICS AND DISSEMINATION: An ethics application is currently under review with the Institute of Health Science Research Ethics Committee, Universiti Brunei Darussalam. All participants are required to provide a written consent form. Findings will be used to identify significant knowledge gaps on AMR aspect in a way that results in lasting change in clinical practice. Moreover, AMR content priorities related to dentistry clinical practice will be determined in order to develop need-based educational resource on microbes, hygiene and prudent antimicrobial use for dentistry programmes.
METHODS: The development of the RAPID guidelines was based on the Guidance for Developers of Health Research Reporting Guidelines. Following a comprehensive search of the literature, the Executive Group identified ten themes in Pediatric Dentistry and compiled a draft checklist of items under each theme. The themes were categorized as: General, Oral Medicine, Pathology and Radiology, Children with Special Health Care Needs, Sedation and Hospital Dentistry, Behavior Guidance, Dental Caries, Preventive and Restorative Dentistry, Pulp Therapy, Traumatology, and Interceptive Orthodontics. A RAPID Delphi Group (RDG) was formed comprising of 69 members from 15 countries across six continents. Items were scored using a 9-point rating Likert scale. Items achieving a score of seven and above, marked by at least 70% of RDG members were accepted into the RAPID checklist items. Weighted mean scores were calculated for each item. Statistical significance was set at p
Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.