Otolaryngology

AI-generated versus standard informed consent forms in otolaryngology: a blinded multirater pilot feasibility study.

BACKGROUND: Informed consent documents often exceed recommended readability levels, potentially compromising patients' decision-making. AIMS/OBJECTIVES: This pilot feasibility study evaluated two large language models (LLMs), ChatGPT-5.2 and Gemini 3 Pro, as tools for generating more comprehensible otolaryngology surgical consent forms, and estimated effect-size parameters for a future trial. MATE...

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.

BACKGROUND: Laryngeal cancer is a significant global health issue with high mortality, and early diagnosis is critical for survival. Developing accurate diagnostic models for laryngoscopy can reduce potential repeated biopsies and lessen the patient burden, representing an urgent clinical need. However, existing artificial intelligence models often function as black boxes and are trained on single...

Performance of Large Language Models in Answering Otolaryngology Clinical Questions in Non-English Languages.

BACKGROUND: Large language models (LLMs) such as ChatGPT are being explored for various medical applications, but their performance across languages i...

Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma Using a Deep Learning Radiomics Model Based on SAM3 Automatic Segmentation of Ultrasound Images: A Multicenter Cohort Study.

RATIONALE AND OBJECTIVES: We aimed to establish a Segment Anything Model 3 (SAM3) based on ultrasound images for automatic papillary thyroid microcarc...

Large Language Models as Health Information Tools: Patient Use and Trust in Otolaryngology.

OBJECTIVES: Patients increasingly use large language models (LLMs) for health information, yet their use patterns, perceptions, and impact on clinical...

Emerging Ethical Considerations in Otolaryngology-Head and Neck Surgery.

OBJECTIVES: To perform a scoping review of ethics-based literature published in the last 5 years in otolaryngology-head and neck surgery (OHNS), and t...

Digital twins: a call for an under-developed tool in otolaryngology-head and neck surgery.

AI meets sleep surgery: assessing drug-induced sleep endoscopy interpretation with a large language model.

STUDY OBJECTIVES: To evaluate the performance and safety of a large language model in interpreting drug-induced sleep endoscopy (DISE) videos and prov...

Clinical and Sustainability Outcomes of Humanitarian Otolaryngology Programs: A Scoping Review.

OBJECTIVE: To investigate clinical outcomes, safety, and sustainability of humanitarian otolaryngology outreach programs in low- and middle-income cou...

Airway coach project: development of a machine learning-based model using clinical and ultrasound parameters to support videolaryngoscopy strategy.

BACKGROUND: Videolaryngoscopy (VL) is recommended as a first-line technique for tracheal intubation; however, existing airway assessment tools-largely...

The Utility of Large Language Models to Assist With Emergency Triage Decisions Within Otolaryngology.

OBJECTIVE: To determine whether contemporary large language models can match clinician performance in evaluating the urgency of emergency otolaryngolo...

Expert Consensus Statement on Acoustic Metrics for Swallowing Dysfunction Screening in Older Adults: A Delphi Study.

OBJECTIVE: Swallowing dysfunction poses significant health risks for older adults. Early detection is crucial to prevent complications such as aspirat...

A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study.

BACKGROUND: Deep learning integrated with ultrasound systems may assist in predicting difficult airway, a life-threatening complication in anesthesia....

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...

Large Language Model Responses to Common Otolaryngological Questions: Evaluating Accuracy, Appropriateness, Readability, and Hallucinations.

OBJECTIVES: Artificial Intelligence (AI) is increasingly integrated into medicine, including otolaryngology. However, concerns remain regarding the ac...

ROI-guided virtual narrow band imaging for laryngeal cancer screening.

White light laryngoscopy is widely available but can miss subtle vascular changes associated with early laryngeal neoplasia. We developed a region-of-...

A comparative analysis of large language models for providing oral cavity cancer information.

This study aimed to comparatively evaluate the medical information delivery capacity and content quality of current large language models (LLMs), spec...

Predictors of the Efficacy of Focused Ultrasound for Moderate-to-Severe Persistent Allergic Rhinitis: A Retrospective Cohort Study.

BACKGROUND: Focused ultrasound (FUS) has achieved favorable results in the treatment of allergic rhinitis (AR). However, some patients still have poor...

Ambient Artificial Intelligence Scribes: A Scoping Review With Implications for Otolaryngology.

BACKGROUND/OBJECTIVE: Ambient artificial intelligence scribing, "ambient AI," is expanding across ambulatory specialties. Despite adoption, the impact...

Prediction of transient hypocalcemia following total thyroidectomy using machine learning methods.