This study evaluates the quality and readability of informed consent documents generated by AI platforms ChatGPT-4 and Bard Gemini Advanced compared to those written by a first-year oral surgery resident for common oral surgery procedures. The evaluation, conducted by 18 experienced oral and maxillofacial surgeons, assessed consents for accuracy, completeness, readability, and overall quality. ChatGPT-4 consistently outperformed both Bard and human-written consents. ChatGPT-4 consents had a median accuracy score of 4 [IQR 4-4], compared to Bard's 3 [IQR 3-4] and human's 4 [IQR 3-4]. Completeness scores were higher for ChatGPT-4 (4 [IQR 4-5]) than Bard (3 [IQR 3-4]) and human (4 [IQR 3-4]). Readability was also superior for ChatGPT-4, with a median score of 4 [IQR 4-5] compared to Bard and human consents, both at 4 [IQR 4-4] and 4 [IQR 3-4], respectively. The Gunning Fog Index for ChatGPT-4 was 17.2 [IQR 16.5-18.2], better than Bard's 23.1 [IQR 20.5-24.7] and the human consents' 20 [IQR 19.2-20.9]. Overall, ChatGPT-4's consents received the highest quality ratings, underscoring AI's potential in enhancing patient communication and the informed consent process. The study suggests AI can reduce misinformation risks and improve patient understanding, but continuous evaluation, oversight, and patient feedback integration are crucial to ensure the effectiveness and appropriateness of AI-generated content in clinical practice.

Background: The purpose of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), and radiomic metrics (quantitative descriptors of image content) extracted from MRI sequences by machine learning increase the efficacy of proper presurgical differentiation between benign and malignant salivary gland tumors. Methods: A retrospective study of 117 patients with salivary gland tumors was conducted between January 2015 and November 2022. Univariate analyses with nonparametric tests and multivariate analyses with machine learning approaches were used. Results: Inflammatory biomarkers showed statistically significant differences (p < 0.05) in the Kruskal–Wallis test based on median values in discriminating Warthin tumors from pleomorphic adenoma and malignancies. The accuracy of NLR, PLR, SII, and SIRI was 0.88, 0.74, 0.76, and 0.83, respectively. Analysis of radiomic metrics to discriminate Warthin tumors from pleomorphic adenoma and malignancies showed statistically significant differences (p < 0.05) in nine radiomic features. The best multivariate analysis result was obtained from an SVM model with 86% accuracy, 68% sensitivity, and 91% specificity for six features. Conclusions: Inflammatory biomarkers and radiomic features can comparably support a pre-surgical differential diagnosis.

Counseling is considered a first-line conservative therapy with respect to temporomandibular joint disorders (TMJD). Nowadays, 50 to 80% of patients acquire health information from the internet before turning to professionals. The purpose of this study has been to investigate the quality of information about TMJD that patients can obtain from YouTube. A YouTube.com search was conducted using the terms “temporomandibular joint disorder”; “limited movement of the mandible”; and “mandibular joint pain”. The videos identified were assessed independently by two panels of three professional and lay reviewers with HONcode, modified DISCERN (MD) and the global quality scale (GQS). A total of 106 videos were included. The professional reviewers reported a mean HONcode score of 4.148 ± 1.314 and a mean MD score of 2.519 ± 1.267, testifying to a modest general quality of the videos. The mean GQS score was 2.987 ± 1.012 for the professional and 3.469 ± 0.891 for the lay reviewers (p < 0.001). The correlations between the ratings were significant between the reviewers within the same group but not between the two groups. The presence of animations significantly influenced the GQS score expressed by the lay reviewers (p = 0.011) but not that of the professionals (p = 0.640). The quality of the information on TMJD on YouTube is generally of poor quality. Healthcare systems and professionals should be prepared to correct misinformation and build trusting relationships with patients which are based on quality counseling. Similarly, academic institutions should produce quality content that leads patients with TMJD toward a correct diagnostic-therapeutic process.

Objective: To predict the risk of metastatic lymph nodes and the tumor grading related to oral tongue squamous cell carcinoma (OTSCC) through the combination of clinical data with radiomics metrics by computed tomography, and to develop a supportive approach in the management of the lymphatic cervical areas, with particular attention to the early stages (T1−T2). Between March 2016 and February 2020, patients with histologically confirmed OTSCC, treated by partial glossectomy and ipsilateral laterocervical lymphadenectomy and subjected to computed tomography (CT) before surgery, were identified by two centers: 81 patients (49 female and 32 male) with 58 years as the median age (range 19–86 years). Univariate analysis with non-parametric tests and multivariate analysis with machine learning approaches were used. Clinical, hematological parameters and radiological features extracted by CT were considered individually and in combination. All clinical parameters showed statistically significant differences (p < 0.05) for the Kruskal−Wallis test when discriminating both the tumor grading and the metastatic lymph nodes. DOI, PLR, SII, and SIRI showed an accuracy of 0.70 (ROC analysis) when identifying the tumor grading, while an accuracy ≥ 0.78 was shown by DOI, NLR, PLR, SII, and SIRI when discriminating metastatic lymph nodes. In the context of the analysis of radiomics metrics, the original_glszm_HighGrayLevelZoneEmphasis feature was selected for identifying the tumor grading (accuracy of 0.70), while the wavelet_HHH_glrlm_LowGrayLevelRunEmphasis predictor was selected for determining metastatic lymph nodes (accuracy of 0.96). Remarkable findings were also obtained when classifying patients with a machine learning approach. Radiomics features alone can predict tumor grading with an accuracy of 0.76 using a logistic regression model, while an accuracy of 0.82 can be obtained by running a CART algorithm through a combination of three clinical parameters (SIRI, DOI, and PLR) with a radiomics feature (wavelet_LLL_glszm_SizeZoneNonUniformityNormalized). In the context of predicting metastatic lymph nodes, an accuracy of 0.94 was obtained using 15 radiomics features in a logistic regression model, while both CART and CIDT achieved an asymptotic accuracy value of 1.00 using only one radiomics feature. Radiomics features and clinical parameters have an important role in identifying tumor grading and metastatic lymph nodes. Machine learning approaches can be used as an easy-to-use tool to stratify patients with early-stage OTSCC, based on the identification of metastatic and non-metastatic lymph nodes.

Background: Revision rhinoplasty is a technically demanding surgical procedure that can put every surgeon in trouble. The main issue of these cases is often an altered osteocartilaginous framework following over-resection during the first intervention. Moreover, the available septal or auricular cartilage for grafting is usually not enough. This review aims to examine contemporary advances in applications of fresh frozen cartilage in rhinoplasty. Methods: A structured review of the current literature (up to December 2023) was performed on four bibliographic databases: PubMed, EMBASE, Cochrane and Medline. The search terms were combinations of “Rhinoplasty” and “Cartilage Graft”, “Allograft” or “Fresh Frozen Cartilage”. The citations of selected studies and review articles were also evaluated if present. Results: The research resulted in 152 articles, and only ten met the inclusion criteria: nine clinical articles and one in vitro study. One of the ten eligible articles was excluded. Conclusions: Fresh frozen rib cartilage proved to be a viable alternative to autologous rib grafts and irradiated homologous rib graft. Despite the higher costs, FFRG can provide a sufficient amount of tissue for grafting avoiding donor site complications and reducing the operative time and proved to have more chondrocytes and to be less prone to resorption compared to irradiated rib.