Anesthesia and analgesia
-
Anesthesia and analgesia · Aug 2024
Comparative StudyClinical Knowledge and Reasoning Abilities of AI Large Language Models in Anesthesiology: A Comparative Study on the American Board of Anesthesiology Examination.
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-3), Bard, and Generative Pretrained Transformer-3 (GPT-4) have demonstrated remarkable language capabilities. While previous studies have explored their potential in general medical knowledge tasks, here we assess their clinical knowledge and reasoning abilities in a specialized medical context. ⋯ GPT-4 outperformed GPT-3 and Bard on both basic and advanced sections of the written ABA examination, and actual board examiners considered GPT-4 to have a reasonable possibility of passing the real oral examination; these models also exhibit varying degrees of proficiency across distinct topics.
-
Anesthesia and analgesia · Aug 2024
The Accuracy of the Learning-Curve Cumulative Sum Method in Assessing Brachial Plexus Block Competency.
The learning-curve cumulative sum method (LC-CUSUM) and its risk-adjusted form (RA-LC-CUSUM) have been proposed as performance-monitoring methods to assess competency during the learning phase of procedural skills. However, scarce data exist about the method's accuracy. This study aimed to compare the accuracy of LC-CUSUM forms using historical data consisting of sequences of successes and failures in brachial plexus blocks (BPBs) performed by anesthesia residents. ⋯ The LC-CUSUM and RA-LC-CUSUM methods were associated with substantial false-positive and false-negative rates. Also, small lower limits for the 95% CIs around the accuracy measures were observed, indicating that the methods may be inaccurate for high-stakes decisions about resident competency at BPBs.
-
Anesthesia and analgesia · Aug 2024
Observational StudyEndothelium-Derived Extracellular Vesicles Expressing Intercellular Adhesion Molecules Reflect Endothelial Permeability and Sepsis Severity.
Currently, clinical indicators for evaluating endothelial permeability in sepsis are unavailable. Endothelium-derived extracellular vesicles (EDEVs) are emerging as biomarkers of endothelial injury. Platelet endothelial cell adhesion molecule (PECAM) and vascular endothelial (VE)-cadherin are constitutively expressed endothelial intercellular adhesion molecules that regulate intercellular adhesion and permeability. Herein, we investigated the possible association between EDEVs expressing intercellular adhesion molecules (PECAM+ or VE-cadherin+ EDEVs) and endothelial permeability and sepsis severity. ⋯ EDEVs expressing intercellular adhesion molecules (PECAM+ or VE-cadherin+ EDEVs) may reflect increased endothelial permeability and could be valuable diagnostic and prognostic markers for sepsis.
-
Anesthesia and analgesia · Aug 2024
Development of an Artificial Intelligence-Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation.
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) can capture images during the TI process. By using artificial intelligence (AI) to detect airway structures, the start and end points can be freely selected, thus eliminating the inconsistencies. Further deconstructing the process and establishing time-sequence analysis may aid in gaining further understanding of the TI process. ⋯ YOLOv3 is a powerful tool for analyzing images recorded by VLS. By using AI to detect the airway structures, the start and end points can be freely selected, resolving the heterogeneity resulting from the inconsistencies in the TIT cut points across studies. Time-sequence analysis involving the deconstruction of VLS-recorded TI images into several phases should be conducted in further TI research.