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.

To ensure the safety and quality of vaccines, especially the newest RNA-vaccines against COVID-19, is one of the World Health Organization’s current highest priorities.Case description.We report th...

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.