(1) Objectives: This systematic review and meta-analysis aimed to summarize current evidence regarding the prognostic role of perineural invasion (PNI) in patients with oral squamous cell carcinoma (OSCC). (2) Methods: We searched Cochrane Central, ProQuest, PubMed, Scopus, Science Direct, and Web of Science, using relevant keywords to identify eligible articles. Two independent reviewers conducted two-stage screening, data extraction, and quality assessment. The risk of bias was assessed using the Newcastle–Ottawa Scale (NOS) criteria. All analyses were performed using comprehensive meta-analysis (CMA; version 3.3.070) software. (3) Results: The study included 101 published articles encompassing 26,062 patients. The pooled analyses showed that PNI was associated with significantly worse overall survival (OS; HR = 1.45, 95% CI: 1.32–1.58; p < 0.001), worse disease-specific survival (DSS; HR = 1.87, 95% CI: 1.65–2.12; p < 0.001), and worse disease-free survival (DFS; HR = 1.87, 95% CI: 1.65–2.12; p < 0.001). Similarly, both local recurrence-free survival (LRFS) and regional recurrence-free survival (RRFS) were worse in patients with PNI (HR = 2.31, 95% CI: 1.72–3.10, p < 0.001; and HR = 2.04, 95% CI: 1.51–2.74, p < 0.001), respectively. The random-effect estimate of three studies demonstrated that the presence of PNI was associated with worse failure-free survival (FFS; HR = 2.59, 95% CI: 1.12–5.98, p < 0.001). (4) Conclusions: The current evidence suggests that PNI can be used as an independent predictor of the prognosis for patients with OSCC. The presence of PNI was associated with worse OS, DFS, DSS, FFS, and with recurrence. Asian patients and patients with extra-tumoral or peripheral PNI invasion were associated with worse prognosis.

Background: The use of artificial intelligence (AI), especially large language models (LLMs), is increasing in health care, including in dentistry. There has yet to be an assessment of the diagnostic performance of LLMs in oral medicine. Objective: We aimed to compare the effectiveness of ChatGPT (OpenAI) and Microsoft Copilot (integrated within the Microsoft 365 suite) with oral medicine consultants in formulating accurate differential and final diagnoses for oral lesions from written clinical scenarios. Methods: Fifty comprehensive clinical case scenarios including patient age, presenting complaint, history of the presenting complaint, medical history, allergies, intra- and extraoral findings, lesion description, and any additional information including laboratory investigations and specific clinical features were given to three oral medicine consultants, who were asked to formulate a differential diagnosis and a final diagnosis. Specific prompts for the same 50 cases were designed and input into ChatGPT and Copilot to formulate both differential and final diagnoses. The diagnostic accuracy was compared between the LLMs and oral medicine consultants. Results: ChatGPT exhibited the highest accuracy, providing the correct differential diagnoses in 37 of 50 cases (74%). There were no significant differences in the accuracy of providing the correct differential diagnoses between AI models and oral medicine consultants. ChatGPT was as accurate as consultants in making the final diagnoses, but Copilot was significantly less accurate than ChatGPT (P=.015) and one of the oral medicine consultants (P<.001) in providing the correct final diagnosis. Conclusions: ChatGPT and Copilot show promising performance for diagnosing oral medicine pathology in clinical case scenarios to assist dental practitioners. ChatGPT-4 and Copilot are still evolving, but even now, they might provide a significant advantage in the clinical setting as tools to help dental practitioners in their daily practice.

Osteolytic Lesion of the Maxilla in an Undiagnosed Multiple Myeloma Patient Identified Incidentally by Cone Beam Computed Tomography - Article abstract #936585

Saliva has many advantages over blood as a biofluid, so using it for measuring and monitoring antibody responses in COVID-19 would be highly valuable. To assess the value of saliva-based IgG and IgM/IgA antibody testing in COVID-19, this cross-sectional pilot study evaluated the accuracy of salivary and serum IgG and IgM/IgA for detecting mild COVID-19 and their correlation. Fifty-one patients with mild COVID-19 (14-28 days post-symptom onset) were included in the study. Enzyme-linked immunosorbent assays (ELISA) were used to measure IgG and IgM/IgA responses to SARS-CoV-2 spike protein in both serum and saliva samples using a slightly modified protocol for saliva samples. Saliva-based IgG testing had 30% sensitivity and 100% specificity, with a positive predictive value (PPV) of 100% and a negative predictive value (NPV) of 50%. Saliva-based IgM/IgA testing had 13.2% sensitivity and 100% specificity, with a PPV of 100% and an NPV of 28.3%. Blood and saliva IgG values were positively correlated. Saliva currently has limited diagnostic value for COVID-19 testing, at least for mild disease. Nevertheless, the significant positive correlation between blood and saliva IgG titers indicates that saliva might be a complementary biofluid for assessing systemic antibody responses to the virus, especially if the assay is further optimized across the full disease spectrum.

Introduction: Teledentistry is an emerging tool to exchange medical information and clinical images to facilitate the diagnosis, prevention, and treatment of oral diseases and patient assurance and education. Considering the shortage of oral medicine specialists in Saudi Arabia, this study aims to assess the experiences of dental specialists with tele-oral medicine and its potential applicability in addressing this shortage. Materials and methods: This was a pilot, cross-sectional study conducted among specialists in the field of oral medicine from January 2020 to March 2020. A total of 16 preselected cases with oral lesions, including clinical history and images, were developed, validated, and shared via email with study participants. Each case included questions on differential diagnosis, provisional diagnosis, and management. The responses were recorded, analyzed, and presented as means and percentages. Results: A total of 49 subjects participated in this study and more than half were under 40 years of age and two-thirds were women. A total of 23 participants had prior experience with tele-oral medicine, mainly via WhatsApp (95.7%), and these cases were received from patients, their families, friends, or other dentists. For all study cases, the correct diagnosis score ranged between 73.50 and 100%, and correct management ranged between 51 and 98%. Conclusions: Tele-oral medicine is an effective tool that may play an important role in patient management in rural regions with a shortage of oral medicine services. Further studies with larger sample sizes and in collaboration with international centers are warranted to confirm these findings.