METHODS: The OPL session was conducted by two postgraduate students in SCD (as teachers), to final year undergraduate dental students (as learners) (n = 90), supervised by two specialists in SCD-related areas (as supervisors). Vetted online pre- and post-intervention quizzes were conducted before and after the session, respectively, followed by an online validated feedback survey of the students' learning experiences. Meanwhile, a reflective session was conducted between the postgraduate students and supervisors to explore their perceptions of OPL. Quantitative data was analyzed via paired t-test (significance level, 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.
METHODOLOGY: The Clarivate Analytics' Web of Science 'All Databases', Elsevier's Scopus, Google Scholar and PubMed Central were searched to retrieve the 50 most-cited articles in the IEJ published from April 1967 to December 2018. The articles were analysed and information including number of citations, year of publication, contributing authors, institutions and countries, study design, study topic, impact factor and keywords was extracted.
RESULTS: The number of citations of the 50 selected papers varied from 575 to 130 (Web of Science), 656 to164 (Elsevier's Scopus), 1354 to 199 (Google Scholar) and 123 to 3 (PubMed). The majority of papers were published in the year 2001 (n = 7). Amongst 102 authors, the greatest contribution was made by four contributors that included Gulabivala K (n = 4), Ng YL (n = 4), Pitt Ford TR (n = 4) and Wesselink PR (n = 4). The majority of papers originated from the United Kingdom (n = 8) with most contributions from King's College London Dental Institute (UK) and Eastman Dental Hospital, London. Reviews were the most common study design (n = 19) followed by Clinical Research (n = 16) and Basic Research (n = 15). The majority of topics covered by the most-cited articles were Outcome Studies (n = 9), Intracanal medicaments (n = 8), Endodontic microbiology (n = 7) and Canal instrumentation (n = 7). Amongst 76 unique keywords, Endodontics (n = 7), Mineral Trioxide Aggregate (MTA) (n = 7) and Root Canal Treatment (n = 7) were the most frequently used.
CONCLUSION: This is the first study to identify and analyse the top 50 most-cited articles in a specific professional journal within Dentistry. The analysis has revealed information regarding the development of the IEJ over time as well as scientific progress in the field of Endodontology.