OBJECTIVES: Since the release of ChatGPT-4 in March 2023, large language models (LLMs) application in biomedical manuscript production has been widespread. GPT-modified text detectors, such as GPTzero, lack sensitivity and reliability and do not quan...
Foundation models (FMs) are general-purpose artificial intelligence (AI) neural networks trained on massive datasets, including code, text, audio, images, and video, to handle myriad tasks from generating texts to analyzing images or composing music....
OBJECTIVES: Ultrasound is a promising low-risk imaging modality that can provide objective airway measurements that may circumvent limitations of drug-induced sleep endoscopy (DISE). This study was devised to identify ultrasound-derived anatomical me...
OBJECTIVE: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of V...
OBJECTIVE: To evaluate and compare the readability and quality of patient information generated by Chat-Generative Pre-Trained Transformer-3.5 (ChatGPT) and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) using validated instru...
OBJECTIVES: Here we describe the development and pilot testing of the first artificial intelligence (AI) software "copilot" to help train novices to competently perform flexible fiberoptic laryngoscopy (FFL) on a mannikin and improve their uptake of ...
OBJECTIVE: The human temporal bone comprises more than 30 identifiable anatomical components. With the demand for precise image interpretation in this complex region, the utilization of artificial intelligence (AI) applications is steadily increasing...
OBJECTIVE: This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria.