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Background Tooth loss is often followed by potential complications, including restricted mouth opening, edema, regional pain, and alveolar osteitis (AO), all of which can impair socket healing and quality of life, compromising the ability to socialize and speak post-extraction. One of the approaches currently used to minimize adverse effects is to use platelet-rich fibrin (PRF) immediately following tooth extraction to reduce these complications. This study aims to evaluate the effectiveness of platelet-rich fibrin in promoting soft and hard tissue healing, and its effects on post-extraction complications. Method A systematic review and meta-analysis were conducted, following an electronic and manual search of PubMed, EMBASE, Web of Science, and Scopus databases. A total of 455 articles were screened, of which 41 articles underwent systematic review, and 21 studies (701 patients) were included for meta-analysis. Statistical analyses were performed using Comprehensive Meta-Analysis software. The risk of bias was evaluated by three assessors based on five domains. To evaluate PRF effectiveness, the focus was on split-mouth designs to minimize inter-patient variability. Result PRF significantly improved soft tissue healing (eight studies, p  < 0.05), reduced AO incidence (four studies, p  < 0.05), and lowered post-operative pain (eleven studies, p  < 0.05). However, PRF did not demonstrate statistically significant improvement in bone healing (three studies, p  > 0.05). Conclusion The analysis showed that PRF significantly improved soft tissue healing and reduced the incidence of AO. While PRF helped reduce pain, the results were inconsistent and influenced by the timing of post-operative assessments. The impact on hard tissue healing was controversial, and future research should explore alternative methods to evaluate PRF’s effects on bone regeneration.

Aim ChatGPT, a large language model (LLM) developed by OpenAI, is designed to generate human-like responses through the analysis of textual data. This study aimed to assess the accuracy and diagnostic capability of ChatGPT-4 in answering clinical scenario-based questions regarding oral lesions. Methods The study included 133 multiple-choice questions (MCQs), each consisting of five possible answers, randomly selected from the Clinical Guide to Oral Disease . Two oral medicine specialists reviewed the answers in the book to ensure accuracy. A general dentist categorized the questions into three levels of difficulty, and two oral medicine specialists validated these categorizations. At each level of difficulty, 37 questions were randomly selected. Consequently, the final questionnaire, consisting of a total of 111 questions categorized by difficulty level, was prepared. The process of asking questions began using the ‘’new message’’ command, to minimize potential bias (influence of prior answers), the researchers manually cleared the chat history before presenting each new question. Result ChatGPT-4.0 demonstrated an accuracy rate of 97% for easy questions, 86.5% ± 34.6% for medium-level questions, and 78.4% ± 41.7% for difficult questions, with an overall accuracy rate of 87.4% ± 33.3. Conclusion Although ChatGPT-4.0 demonstrated satisfactory accuracy in answering clinical questions, its responses should not be exclusively relied upon for diagnostic purposes. Instead, the model should be utilized as a complementary tool under the supervision of clinicians in the diagnosis of oral lesions.