Latest AI and machine learning research in otolaryngology for healthcare professionals.
Successful artificial intelligence (AI) implementation is predicated on the trust of clinicians and patients, and is achieved through a culture of responsible use, focusing on regulations, standards, and education. Otolaryngologists can overcome barriers in AI implementation by promoting data standardization through professional societies, engaging in institutional efforts to integrate AI, and dev...
To fuel artificial intelligence (AI) potential in clinical practice in otolaryngology, researchers must understand its epistemic limitations, which are tightly linked to ethical dilemmas requiring careful consideration. AI tools are fundamentally opaque systems, though there are methods to increase explainability and transparency. Reproducibility and replicability limitations can be overcomed by s...
The increasing development of artificial intelligence (AI) generative models in otolaryngology-head and neck surgery will progressively change our pra...
OBJECTIVE: Develop an artificial intelligence assisted computer vision model to screen for laryngeal cancer during flexible laryngoscopy.
OBJECTIVE: The recommended readability of health education materials is at the sixth-grade level. Artificial intelligence (AI) large language models s...
Use of artificial intelligence (AI) is expanding exponentially as it pertains to workflow operations. Otolaryngology-Head and Neck Surgery (OHNS), as ...
Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. S...
PURPOSE: Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a...
To evaluate the response capabilities, in a public healthcare system otolaryngology job competition examination, of ChatGPT 3.5 and an internet-connec...
OBJECTIVE: The primary focus of this study was to analyze the adoption of ChatGPT among Ear, Nose, and Throat (ENT) trainees, encompassing its role in...
Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely ba...
Otolaryngologists can enhance workflow efficiency, provide better patient care, and advance medical research and education by integrating artificial i...
INTRODUCTION: Letters of recommendation (LORs) are a highly influential yet subjective and often enigmatic aspect of the residency application process...
Many open-access artificial intelligence (AI)-based websites that rate facial attractiveness are available, but none have been compared with human fo...
PURPOSE: With recent advances in artificial intelligence (AI), it has become crucial to thoroughly evaluate its applicability in healthcare. This stud...
BACKGROUND: Endoscopic transaxillary approaches to thyroidectomy have been well described and gasless transaxillary endoscopic thyroidectomy (GTET) is...
BACKGROUND: Robotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This st...
BACKGROUND: The extent of postoperative pain following transoral thyroidectomy is not well-understood and remains a subject of debate. This study aims...
BACKGROUND: Machine learning driven clinical decision support tools (ML-CDST) are on the verge of being integrated into clinical settings, including i...