Background: AI has been adopted in dentistry for diagnosis, decision making, and therapy prognosis prediction. This systematic review aimed to identify AI models in dentistry, assess their performance, identify their shortcomings, and discuss their potential for adoption and integration in dental practice in the future. Methodology: The sources of the papers were the following electronic databases: PubMed, Scopus, and Cochrane Library. A total of 20 out of 947 needed further studies, and this was encompassed in the present meta-analysis. It identified diagnostic accuracy, predictive performance, and potential biases. Results: AI models demonstrated an overall diagnostic accuracy of 82%, primarily leveraging artificial neural networks (ANNs) and convolutional neural networks (CNNs). These models have significantly improved the diagnostic precision for dental caries compared with traditional methods. Moreover, they have shown potential in detecting and managing conditions such as bone loss, malignant lesions, vertical root fractures, apical lesions, salivary gland disorders, and maxillofacial cysts, as well as in performing orthodontic assessments. However, the integration of AI systems into dentistry poses challenges, including potential data biases, cost implications, technical requirements, and ethical concerns such as patient data security and informed consent. AI models may also underperform when faced with limited or skewed datasets, thus underscoring the importance of robust training and validation procedures. Conclusions: AI has the potential to revolutionize dentistry by significantly improving diagnostic accuracy and treatment planning. However, before integrating this tool into clinical practice, a critical assessment of its advantages, disadvantages, and utility or ethical issues must be established. Future studies should aim to eradicate existing barriers and enhance the model’s ease of understanding and challenges regarding expense and data protection, to ensure the effective utilization of AI in dental healthcare.

Certainty, artificial intelligence (AI) has a remarkable role in the control of the COVID-19 pandemic globally. This review article aimed to determine the outcomes/benefits associated with the use of artificial intelligence during the COVID-19 pandemic in different specialties in healthcare. Out of 144 articles, 100 publish article abstracts were reviewed using keywords artificial intelligence, intelligent health systems during COVID-19 pandemic, medical education. This review determined that artificial intelligence has a significant role in predicting the spread of disease by chasing the infected population and can prove as a powerful tool for public health professionals. There is a remarkable role of AI-assisted diagnostic features in radiology to specifically diagnose infected cases with COVID-19. AI assists in taking quicker decision making among health professionals through AI operated apps.This review highlights the need for future research which should emphasize determining the effectiveness and challenges of the use of robotics during pandemics.

Oral health significantly impacts overall health, with increasing evidence linking poor oral health to adverse pregnancy outcomes. Periodontal disease, a chronic gum condition, is associated with preterm birth, low birth weight, and preeclampsia. Systemic inflammation and bacterial translocation are proposed mechanisms connecting periodontal disease to pregnancy complications. Maternal immune responses may be impaired, increasing systemic inflammation, and triggering preterm labor. In addition, oral bacteria may reach the uterus, causing localized inflammation and poor outcomes. Intervention trials show mixed results—some small studies report improved birth outcomes after periodontal treatment, while larger trials show no significant effect. These discrepancies highlight the need for further research on patient subgroups, disease categories, and treatment strategies. Prenatal care should emphasize preventive dental care, including regular checkups, good oral hygiene, and treatment of periodontal disease. Addressing oral health during pregnancy can improve outcomes for both mother and child. Future studies should explore the oral‐systemic connection, host susceptibility, and effective interventions for at‐risk individuals. Comprehensive oral health care during pregnancy offers the potential to reduce adverse outcomes and promote better maternal and fetal health.

Background: The transformation of theoretical knowledge into effective patient care is made possible by early exposure to clinical practice. It makes a substantial contribution to helping students acquire basic competencies such as empathy, problem‐solving, and communication. The current study intends to determine if improved clinical interaction improves knowledge acquisition and skill acquisition, as well as to support the effect of early preclinical exposure (EPCE) on the academic performance of undergraduate dental students. Objectives: To assess the effects of EPCE on academic performance, soft skills, and confidence levels among second‐year BDS students at HBS Dental College, Pakistan. Methodology: This comparative cross‐sectional survey included a total of 150 BDS students, who were divided into two groups: Group A (EPCE) and Group B (late exposure). Academic performance was assessed using scores from the Objective Structured Clinical Examination (OSCE) and theoretical written exams. To evaluate the development of soft skills and self‐confidence, both groups completed a common set of self‐administered questionnaire items designed for this purpose. Descriptive statistics, including means and standard deviations, were calculated. Independent sample t ‐tests were used to compare outcomes between the two groups. A p value < 0.05 was considered statistically significant. Results: A total of 150 students were enrolled (75 per group). Group A (early exposure) demonstrated significantly higher performance across all domains. Theory exam scores were higher in Group A (82%, SD = 3.5) than Group B (76%, SD = 4.2; p = 0.002, Cohen’s d = 1.55). In the OSCE, Group A outperformed in communication (85% vs. 78%, p = 0.004, d = 2.15) and empathy (83% vs. 75%, p = 0.005, d = 2.28). Self‐reported confidence was also greater in Group A (84% vs. 77%, p < 0.005, d = 2.00). Conclusion: The implementation of early preclinical placements significantly enhanced academic achievement and professional development among BDS students. Integrating early clinical exposure into the dental curriculum enriches the overall educational experience and better prepares students for the demands of real‐world dental practice.

As the successful establishment of technology supported educational systems requires wide investment in terms of finances and faculty time, this study explores the influencing factors in the adoption of Technology Enhanced Learning (TEL) and the main barriers encountered during the use of TEL in Punjab, Pakistan. Semi-structured interviews were conducted with medical educators and thematic analysis was carried out using Nvivo-10. The study participants were well aware of educational technologies and stated that the influencing factors for adoption of TEL were self-effort, motivation, a personal positive approach and departmental policy. The institutions were well equipped with resources but the main problem was the lack of faculty training and institutional support. The study concludes that pedagogy is the main driver for the use of educational technologies. At the national level, it is suggested that governing bodies can provide definite polices and guidelines for the implementation of TEL. Therefore, it is advocated that recognizing online courses can enhance potential learners' interest to enroll for e-learning programmes. At the institutional level, it is proposed to encourage the establishment of institutional policies and the organization of workshops for faculty training. At an individual level, it is stressed that there is a basic requirement to develop a technology-oriented culture partly by sharing the power and expertise among the department heads and faculty members.

Background: Oral squamous cell carcinoma (OSCC) may arise from premalignant oral lesions (PMOL) in most cases. Minichromosome maintenance 3 (MCM3) is a proliferative marker that has been investigated as a potential diagnostic biomarker in the diagnosis of oral cancer. Objectives: To evaluate the association of MCM3 expression, its clinicopathologic parameters and to identify snuff (also called naswar) as a potential risk factor for changes in MCM3 expression in PMOL and OSCC. Methodology: Immunohistochemistry (IHC) of MCM3 was performed on 32 PMOL, 32 OSCC and 16 normal controls after optimization of IHC methodology. Histoscore (0–300) was used as a scoring system and seven different cut-offs were identified for analyses. Data were analyzed using various statistical tests. Results: Among the seven cutoffs, 40% strong positive cells were found to be a better cut-off as they were associated with many pathological variables (Broder’s grade, Aneroth’s grade, and mitotic activity). The differential MCM3 expression in oral lesions (PMOL and OSCC) was statistically significant (p = 0.03). Moreover, MCM3 expression is raised with increased duration and frequency of snuff use. Conclusion: High MCM3 expression is associated with disease progression and is a potential indicator of malignant transformations from PMOL to OSCC. Moreover, the use of snuff is associated with MCM3 over-expression.