Latest AI and machine learning research in otolaryngology for healthcare professionals.
OBJECTIVE: Artificial Intelligence (AI) research needs to be clinician led; however, expertise typically lies outside their skill set. Collaborations exist but are often commercially driven. Free and open-source computational algorithms and software expertise are required for meaningful clinically driven AI medical research. Deep learning algorithms automate segmenting regions of interest for anal...
BACKGROUND: Volumetric atlases are an invaluable tool in neuroscience and otolaryngology, greatly aiding experiment planning and surgical interventions, as well as the interpretation of experimental and clinical data. The rat is a major animal model for hearing and balance studies, and a detailed volumetric atlas for the rat central auditory system (Waxholm) is available. However, the Waxholm rat ...
Event cameras respond to changes in log-brightness at the millisecond level, making them ideal for optical flow estimation. However, existing datasets...
OBJECTIVE: Traditional evaluations of surgical skills in otolaryngology rely heavily on subjective assessments, which are prone to variability and bia...
OBJECTIVE: To develop and evaluate the effectiveness of domain-specific customization in large language models (LLMs) by assessing the performance of ...
OBJECTIVE: To review the current literature on the applications of natural language processing (NLP) within the field of otolaryngology.
OBJECTIVES: Since the release of ChatGPT-4 in March 2023, large language models (LLMs) application in biomedical manuscript production has been widesp...
PURPOSE: This study aimed to explore the capabilities of advanced large language models (LLMs), including OpenAI's GPT-4 variants, Google's Gemini ser...
OBJECTIVES: ChatGPT is one of the most publicly available artificial intelligence (AI) softwares. Ear, nose and throat (ENT) services are often stretc...
BACKGROUND AND OBJECTIVES: Recently, artificial intelligence (AI) has been applied to otolaryngology. However, existing supervised learning methods ca...
BACKGROUND: Emergency endotracheal intubation is a critical skill for managing airway emergencies in the emergency department (ED). Accurate predictio...
Foundation models (FMs) are general-purpose artificial intelligence (AI) neural networks trained on massive datasets, including code, text, audio, ima...
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogr...
Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imagin...
Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images rela...
BACKGROUND: The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band im...
This report synthesizes the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Task Force's guidance on the integration of artificial ...
OBJECTIVE: This study aimed to develop an artificial intelligence (AI) method to augment video laryngoscopy (VL) by automating the detection of the gl...
BACKGROUND: The early diagnosis of laryngeal cancer (LCA) is crucial for prognosis, driving our search for an accurate, precise, and sensitive deep le...
OBJECTIVE: To evaluate and compare the readability and quality of patient information generated by Chat-Generative Pre-Trained Transformer-3.5 (ChatGP...