The first mention of the use of hydraulic cements in dentistry dates to the 19^th century with Portland cement used as an additive to make a paste for endodontic use. This did not gain popularity until the mid-1990s when the first patent for the use of Portland cement for root-end surgery and perforation repair was filed by Torabinejad and White and the first material marketed by Dentsply as ProRoot MTA. Even though the term mineral trioxide aggregate (MTA) is a misnomer as MTA is neither an oxide nor an aggregate, several developments have resulted in a number of materials being created for clinical endodontics. These materials are presented in various consistencies and delivery methods. Furthermore, although composed primarily of tricalcium and dicalcium silicate, the materials also include a radiopacifier, additives and an aqueous or a non-aqueous vehicle Only materials whose primary reaction is with water can be classified as hydraulic. The main features of hydraulic cements are the formation of calcium hydroxide on hydration and the interaction of these materials with the environment. The current review refers to a classification by Camilleri and the literature pertaining to specific uses of hydraulic cements in endodontics namely intra-coronal, intra-radicular and extra-radicular as this classification informs the clinician about the material interactions which are dependent on the environment the material is placed in. The literature was reviewed using PUBMED and for each clinical use, the in vitro properties such as physical, chemical, biological and antimicrobial characteristics and clinical data was extracted and evaluated. This provided the current status of these materials after which future trends and gaps in knowledge were identified. The hydraulic cements have made a difference to clinical outcomes. The main shortcoming is the poor testing methodologies employed which provide very limited information and also inhibits adequate clinical translation. Furthermore, the clinical protocols need to be updated to enable the materials to be employed effectively.

Advances in artificial intelligence (AI) technology have generated a revolution in medical and dental education, which may offer promising solutions to tackle the challenges of traditional problem-based learning (PBL) and case-based learning (CBL). The objective of this study was to assess the available evidence concerning AI-powered PBL/CBL on students' knowledge acquisition, clinical reasoning capability and satisfaction. An electronic search was carried out on PubMed, MEDLINE, the Cochrane Central Register of Controlled Trials and Web of Science. Clinical trials published in English with full text available, which implemented AI technologies in PBL/CBL in the medical/dental field and evaluated knowledge acquisition, clinical reasoning and/or satisfaction were included. The quality assessment was conducted using RoB 2 by two calibrated assessors. Data synthesis and meta-analysis were performed, the standardised mean difference (SMD) or standardised mean (SM) and 95% confidence intervals (CIs) were calculated, and heterogeneity was quantified. Six randomized controlled trials were included, with an overall risk of bias judged to have 'some concerns'. For knowledge acquisition, 4 studies were included in the meta-analysis. A low heterogeneity (I² = 20%) was detected and a fixed-effect model was utilised. Compared with the control group, the AI intervention significantly improved knowledge acquisition by 46% (95% Cls [0.18-0.73], P = .001). For clinical reasoning capability, due to methodological and measurement heterogeneity among studies, statistical analysis was not feasible. Three studies were selected for the meta-analysis of students' satisfaction. Heterogeneity was moderate (I² = 32%), and a generic inverse variance method was selected. The pooled SM score was 0.7 (95% Cls [0.47-0.92]), and the overall effect was statistically significant (P < .00001). Despite limitations such as the limited number of included studies and the overall risk of bias concerns, AI-powered PBL/CBL has the potential to enhance students' knowledge acquisition and learner satisfaction compared to traditional learning approaches. Not applicable.