The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about the potential and challenges of integrating AI into audiological practice. The PubMed, Cochrane, and Google Scholar databases were searched for articles published in English from 1990 to 2024 with the following query: “(audiology) AND (“artificial intelligence” OR “machine learning” OR “deep learning”)”. The PRISMA extension for scoping reviews (PRISMA-ScR) was followed. The database research yielded 1359 results, and the selection process led to the inclusion of 104 manuscripts. The integration of AI in audiology has evolved significantly over the succeeding decades, with 87.5% of manuscripts published in the last 4 years. Most types of AI were consistently used for specific purposes, such as logistic regression and other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) for automated audiometry and clinical predictions; convolutional neural networks for radiological image analysis; and large language models for automatic generation of diagnostic reports. Despite the advances in AI technologies, different ethical and professional challenges are still present, underscoring the need for larger, more diverse data collection and bioethics studies in the field of audiology.

Background: In the evolving field of maxillofacial surgery, integrating advanced technologies like Large Language Models (LLMs) into medical practices, especially for trauma triage, presents a promising yet largely unexplored potential. This study aimed to evaluate the feasibility of using LLMs for triaging complex maxillofacial trauma cases by comparing their performance against the expertise of a tertiary referral center. Methods: Utilizing a comprehensive review of patient records in a tertiary referral center over a year-long period, standardized prompts detailing patient demographics, injury characteristics, and medical histories were created. These prompts were used to assess the triage suggestions of ChatGPT 4.0 and Google GEMINI against the center’s recommendations, supplemented by evaluating the AI’s performance using the QAMAI and AIPI questionnaires. Results: The results in 10 cases of major maxillofacial trauma indicated moderate agreement rates between LLM recommendations and the referral center, with some variances in the suggestion of appropriate examinations (70% ChatGPT and 50% GEMINI) and treatment plans (60% ChatGPT and 45% GEMINI). Notably, the study found no statistically significant differences in several areas of the questionnaires, except in the diagnosis accuracy (GEMINI: 3.30, ChatGPT: 2.30; p = 0.032) and relevance of the recommendations (GEMINI: 2.90, ChatGPT: 3.50; p = 0.021). A Spearman correlation analysis highlighted significant correlations within the two questionnaires, specifically between the QAMAI total score and AIPI treatment scores (rho = 0.767, p = 0.010). Conclusions: This exploratory investigation underscores the potential of LLMs in enhancing clinical decision making for maxillofacial trauma cases, indicating a need for further research to refine their application in healthcare settings.

Surgical navigation technology combines patient imaging studies with intraoperative real-time data to improve surgical precision and patient outcomes. The navigation workflow can also include preoperative planning, which can reliably simulate the intended resection and reconstruction. The advantage of this approach in skull-base surgery is that it guides access into a complex three-dimensional area and orients tumors intraoperatively with regard to critical structures, such as the orbit, carotid artery and brain. This enhances a surgeon’s capabilities to preserve normal anatomy while resecting tumors with adequate margins. The aim of this narrative review is to outline the state of the art and the future directions of surgical navigation in the skull base, focusing on the advantages and pitfalls of this technique. We will also present our group experience in this field, within the frame of the current research trends.

Background and Objectives: Sudden Sensorineural Hearing Loss (SSNHL) is a quite common clinical finding in otolaryngology. Most cases are classified as idiopathic and there is a dearth of information on factors able to predict the response to treatment and hearing recovery. The main aim of this systematic review and meta-analysis was to assess and critically discuss the role of circulating inflammatory biomarkers in SSNHL. Materials and Methods: A search was conducted of the English literature published between 1 January 2009 and 7 July 2022 on Pubmed, Scopus, Web of Science, ScienceDirect, and Cochrane following PRISMA guidelines. Results: A total of 256 titles were retrieved from the search. After full-text screening and application of inclusion/exclusion criteria, 13 articles were included. Twelve out of thirteen studies reported significant differences in biomarkers values in SSNHL patients, of which Tumor Necrosis Factor alpha (TNF-α) and C-reactive Protein (CRP) were the most analyzed. Our meta-analysis for CRP’s mean values in SSNHL groups vs. controls showed significantly higher CRP levels with a pooled overall difference of 1.07; confidence interval (CI) at 95%: 0.03; 2.11. For TNF-α, discordant results were found: three studies showed significantly higher levels in SSNHL patients vs. controls, whereas other three investigations showed lower levels in the SSNHL groups (overall pooled difference 1.97; 95% CI: −0.90; 4.84). A high between-study heterogeneity was found. Conclusions: This systematic review pointed out that, although there exists a growing literature in the field of circulatory biomarkers identification in SSNHL, there is a high heterogeneity of results and low quality of evidence. CRP resulted to be higher in SSNHL patients than in controls, while TNF-α showed more heterogeneous behavior. The data reported herein needs to be confirmed in well-designed prospective multicenter randomized studies, with the objective of improving SSNHL treatment and outcome and thereby reducing the social burden of hearing loss.