Among various clinical skills, critical thinking (CT) is foundational for quality patient care. However, existing literature suggests deficiencies in applying analytical reasoning skills in healthcare education. As CT integration gains momentum in dentistry, the rapid advancement of artificial intelligence (AI) technologies introduces new dynamics. This article explores the potential impacts of emerging technologies of AI on the development and application of CT in the dental profession. A narrative literature review of English-language publications was conducted using sources such as PubMed, MEDLINE, ScienceDirect, Wiley-Online-Library, and Oral-Health-Group. Our review focused on relevant articles published up to June 30,2024, representing a peak period of CT awareness. The search terms used included: "Critical thinking," "healthcare," "rubric," "dental education," and "Artificial intelligence." No inclusion or exclusion criteria were applied. The search strategies aimed to synthesize collective healthcare experience in implementing CT in education, providing a cohesive understanding of the current state of CT and highlighting gaps in its integration in education. Strategies for incorporating CT into dental education are still developing. Although dental educators recognize the importance of CT-based curricula, barriers hinder effective implementation. Additionally, the rise of advanced technologies with human-like cognitive and non-cognitive capabilities necessitates agile, adaptable strategies to integrate CT in dental education. A simplified model is proposed for integrating AI into the development of CT skills at multiple levels in dental education. Furthermore, a rubric for assessing CT skills in problem-based learning is presented. This review emphasizes the urgent need to strategically prioritize CT integration in dental education. Inadequate application of CT may increase the risk of AI misuse, and overreliance on AI could potentially undermine clinical decision-making. We propose that emerging technologies should be embraced as tools to enhance the development of CT skills while mitigating potential negative impacts. Plausible approaches to addressing this challenge are outlined.

This article explores the potential benefits of Artificial Intelligence (AI) and Machine Learning (ML) in Orthodontics, highlighting their efficiency and accuracy. While AI has influenced various fields, its application in orthodontics is just being explored. With the innovation comes challenges that are associated with AI. This article emphasizes the documented role of AI and its associated barriers in Orthodontics. Literature research is performed in data sources like online library journals PubMed and MEDLINE, NIH (National Institute of Health), Science Direct, WILEY online library, and ORAL HEALTH GROUP, among others. Our review was carried out on articles published to date. The findings in this review highlight the considerable promise of employing AI within orthodontics. However, the emergence of AI also brings forth fresh challenges that must be considered. Striking a balance between innovation and addressing these challenges is crucial for advancing orthodontics.

Objectives: There is a growing interest among practitioners in employing artificial intelligence (AI) to enhance the precision and efficiency of diagnostic methods. The objective of this study is to assess the precision of an AI-based method for facial asymmetry assessment using 3D facial images. Methods: The study included 130 patients (84 female, 46 male), analyzing 3D facial images from the Vectra^® M3 imaging system using both manual and AI-based methods. Seven bilateral facial landmarks were identified for manual analysis, calculating the asymmetry index for facial symmetry assessment. An AI-based program was developed to automate the identification of the same landmarks and calculate the asymmetry index. The reliability of the manual measurements was assessed using intraclass correlation coefficients (ICC) with 95% confidence intervals (CI). Precision of automated landmark identification was compared to the manual method. Results: The ICCs for the manual measurements demonstrated moderate to excellent reliability, both within raters (ICC = 0.62–0.99) and between raters (ICC = 0.72–0.96) each calculated with 95% CI. Agreement was observed between the manual and automated methods in calculating the asymmetry index for five landmarks. There was a statistically significant difference between the two methods in determining the asymmetry index for alare (median: 2.05 mm manual vs. 1.54 mm automated, p = 0.0056) and cheilion (median: 2.77 mm manual vs. 2.30 mm automated, p = 0.0081). Conclusions: The AI-based method provides efficient and comparable precision of facial asymmetry analysis using 3D images. The disagreement observed between the two methods can be addressed through further improvement and training of the automated software. This innovative approach opens doors to significant advancements in both research and clinical orthodontics.

Complete denture fabrication requires multiple clinical and laboratory steps. One of the most critical clinical steps is establishing an anatomical occlusal plane based on hard and soft tissue references. The aim of this study was to determine whether age or gender affects the level of the Ala-Tragus plane to establish which reference point on the Tragus should be used when fabricating the occlusal plane in edentulous patients. Clinical photographs and lateral cephalometric radiographs with complete dentitions were taken from 58 volunteers at the DMD clinic at the University of Kentucky. Each photograph was superimposed over its corresponding cephalometric image. An analysis was conducted to establish the angle of the occlusal plane relative to the Ala-Tragus landmarks; this data was then grouped according to age and gender. The analysis shows that age and gender did not significantly affect where the Camper’s plane should be approximated for complete denture treatment. However, it was found that the most parallel line to the occlusal plane was Ala’s inferior border to the ‘Tragus’s inferior border. It should be noted that the volunteers’ skeletal classification was significantly related to a Cl III malocclusion tendency. Still, with this new information, functionality and esthetics can be more adequately addressed for patients undergoing complete denture treatment. Given our results, we suggest redefining the ‘Camper’s plane with a line extending from ‘Ala’s inferior border to the ‘Tragus’s inferior border instead of the superior border. Further consideration should be taken if the patient is a skeletal CL III malocclusion.

This study examined the association between a dental implant and changes in adjacent teeth over time. Electronic health records of 1818 patients who received a dental implant were retrospectively evaluated over 14 years (2005–2019) in a university setting. The status of the adjacent tooth and vertical and horizontal distance from the implant platform to adjacent teeth were determined using digital intraoral radiographs taken at baseline and the last follow-up visit (1–14 years, median 4 years). In total, 1085 dental implants were evaluated. There were 234 instances of a change in the adjacent tooth. Decay was observed in 83 (7.6%) of adjacent teeth; the mean time to development was 4 years (range 1–14 years). Approximately 9% of adjacent teeth received direct restorations, 4.8% received indirect restorations, 1% received endodontic root canal treatment, and 5.6% were extracted. The mean horizontal distance between the implant platform and the adjacent teeth was 3.56 mm; the mean vertical distance from the contact point to the alveolar crest on the tooth side was 6.2 mm at the first time of the reported decay on X ray. These distances did not significantly influence the occurrence of caries. The prevalence of interproximal contact loss was higher on the mesial of the implant crown at 63% compared with 20% on the distal side. This large retrospective analysis identified that teeth adjacent to a dental implant were at risk of decay and changes in their condition. In addition, the implant-to-tooth distance and inadequate emergence profile may contribute as caries risk factors in addition to hygiene and a high sugar level diet. These findings appear essential for clinicians when making treatment decisions and discussing outcomes with patients.