Latest AI and machine learning research in otolaryngology for healthcare professionals.
STUDY OBJECTIVES: To evaluate the performance and safety of a large language model in interpreting drug-induced sleep endoscopy (DISE) videos and providing treatment recommendations for obstructive sleep apnea, compared to expert human raters and the contemporaneous clinical report. METHODS: This prospective, blinded study included 16 adults undergoing drug-induced sleep endoscopy at a tertiary ac...
OBJECTIVE: To investigate clinical outcomes, safety, and sustainability of humanitarian otolaryngology outreach programs in low- and middle-income countries (LMICs). DATA SOURCES: PubMed, Scopus, and Cochrane databases. REVIEW METHODS: Following PRISMA-ScR guidelines, two investigators searched databases for published studies reporting clinical, safety, and sustainability outcomes of otolaryngolog...
BACKGROUND: Videolaryngoscopy (VL) is recommended as a first-line technique for tracheal intubation; however, existing airway assessment tools-largely...
OBJECTIVE: To determine whether contemporary large language models can match clinician performance in evaluating the urgency of emergency otolaryngolo...
OBJECTIVE: Swallowing dysfunction poses significant health risks for older adults. Early detection is crucial to prevent complications such as aspirat...
BACKGROUND: Deep learning integrated with ultrasound systems may assist in predicting difficult airway, a life-threatening complication in anesthesia....
PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...
OBJECTIVES: Artificial Intelligence (AI) is increasingly integrated into medicine, including otolaryngology. However, concerns remain regarding the ac...
White light laryngoscopy is widely available but can miss subtle vascular changes associated with early laryngeal neoplasia. We developed a region-of-...
This study aimed to comparatively evaluate the medical information delivery capacity and content quality of current large language models (LLMs), spec...
BACKGROUND: Focused ultrasound (FUS) has achieved favorable results in the treatment of allergic rhinitis (AR). However, some patients still have poor...
BACKGROUND/OBJECTIVE: Ambient artificial intelligence scribing, "ambient AI," is expanding across ambulatory specialties. Despite adoption, the impact...
OBJECTIVES: This study aimed to compare the diagnostic accuracy and citation integrity of four large language models (LLMs) including one general (Cha...
OBJECTIVE: Artificial intelligence (AI) has advanced to simultaneously process visual, auditory, and textual inputs, providing users with "multimodal"...
Transient hypocalcemia is a common complication of total thyroidectomy. This study aimed to evaluate whether machine learning (ML)-based models could ...
BACKGROUND: Facial paralysis rehabilitation has progressed substantially over the past two decades, yet the scientific landscape of this field remains...
PurposeTo quantify how large language model (LLM) assistance influences otolaryngology residents' operative planning in a simulation-based setting.Met...
In this study, we present an innovative approach in medicine, leveraging the potential of artificial intelligence (AI) for the diagnosis of tympanic m...
CONTEXT: Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, an...