Articles: anesthesia.
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The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. ⋯ Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.
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The National Health Service (NHS) has pledged to reach carbon net-zero by 2040. In alignment with this goal, a London hospital's anaesthesia department is actively reducing desflurane use and transitioning towards total intravenous anaesthesia (TIVA) as a sustainable alternative, contributing to environmentally responsible practices within the healthcare sector. ⋯ The evaluation examines stakeholder attitudes, perceptions, and behaviours, focusing on transitioning from desflurane to TIVA. The study highlights the importance of staff engagement, organisational support, and underscores the crucial role that healthcare practitioners and leadership play in fostering sustainability within the healthcare sector.
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The rapidly developing field of artificial intelligence (AI) may soon equip clinicians with algorithms that model and predict perioperative problems with extreme accuracy. Here, we outline emerging AI applications in preoperative risk stratification and intraoperative event prediction, where algorithm performance has been shown to outstrip commonly used conventional risk prediction tools. While offering an enticing view of a novel perioperative practice with superhuman foresight, AI's limited scope and lack of transparency remain key challenges for widespread adoption. As yet it is unclear whether machine learning alone can influence human clinical practice to exert real-world effects on patient outcomes.
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There remains significant debate regarding the performance of deep brain stimulation (DBS) procedures for Parkinson disease (PD) under local or general anesthesia. The aim of this meta-analysis was to compare the clinical outcomes between "asleep" DBS (general anesthesia) and "awake" DBS (local anesthesia) for PD. ⋯ There was no significant difference in the primary motor outcomes and LEDD improvement between asleep vs awake DBS. The variables of target selection and MER use had no statistically significant impact on outcome. We find that asleep techniques are both safe and effective compared with the awake technique.