Anesthesiology has a longstanding commitment to patient safety, characterized by innovative research, quality improvement, multidisciplinary collaboration, and engineering-based approaches to care systems. The field has been instrumental in advancing...
Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert ...
Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models usi...
BACKGROUND: Due to their invasiveness, arterial lines are not typically used in routine monitoring, despite their superior responsiveness in hemodynamic monitoring and detecting intraoperative hypotension. To address this issue, noninvasive, continuo...
BACKGROUND: This study presents an analysis of machine-learning model performance in image analysis, with a specific focus on videolaryngoscopy procedures. The research aimed to explore how dataset diversity and size affect the performance of machine...
BACKGROUND: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The objective of this study was to develop machine-learning-based predictive models for persistent opioi...
BACKGROUND: Over the past decade, artificial intelligence (AI) has expanded significantly with increased adoption across various industries, including medicine. Recently, AI-based large language models such as Generative Pretrained Transformer-3 (GPT...
BACKGROUND: Total intubation time (TIT) is an objective indicator of tracheal intubation (TI) difficulties. However, large variations in TIT because of diverse initial and end targets make it difficult to compare studies. A video laryngoscope (VLS) c...