麻醉科医生

This could involve creating simulation ... in anesthetic education and training has the ability to provide trainee anesthesiologists with realistic simulations, individualized learning experiences, and data-driven feedback, allowing them to acquire knowledge more swiftly and ...

The use of AI in anesthesia has been a topic of growing interest in recent years, with several articles addressing the current state of AI in anesthesia and the potential benefits and challenges of incorporating AI into anesthesia care; V Mihir (2022) conducted a questionnaire-based feedback on the support of ML to anesthesiologists, and the anesthesiologist reported that the efficiency of peak glucose level estimation rose from 79.0 ± 13.7% without the assistance of ML to 84.7 ± 11.5% (P < 0.001) when they were provided with ML estimates, and opioid estimation increased from 18% to 42% with the help of ML.

AI-driven tools now contribute to nearly every phase of perioperative care, including preoperative risk stratification, intraoperative monitoring, imaging interpretation, airway assessment, regional anaesthesia, and critical care. Applications such as automated American Society of Anesthesiologists classification, prediction of postoperative complications and intensive care unit needs, electroencephalography-based depth-of-anaesthesia estimation, and proactive haemodynamic management are reshaping clinical decision-making.

This intersection and integration bring unprecedented challenges to anesthesia, and also provide more opportunities for anesthesiologists and researchers to explore and practice. The research and application of AI in anesthesiology are becoming more and more extensive, and preliminary results have been achieved, which are mainly reflected in preoperative anesthesia management, intraoperative drug delivery, pain management, and postoperative complication prediction.

Artificial intelligence AI integration ... possibilities, rapidly extending beyond human competencies and offering enhanced precision, predictive analytics, and real-time decision support to anesthesiologists [4]. Along with leveraging Machine Learning (ML) algorithms, neural networks ...

“I am most excited about the clinical decision support that anesthesiologists will be able to tap into as AI becomes more refined and robust,” says Chris Giordano, M.D. “I haven’t met an anesthesiologist who wouldn’t like to have more information at their fingertips to help better understand situations and likelihoods.

The algorithm’s advances increase the feasibility for computers to maintain patient unconsciousness with no more drug than is needed, thereby freeing up anesthesiologists for all the other responsibilities they have in the operating room, including making sure patients remain immobile, experience no pain, remain physiologically stable, and receive adequate oxygen, say co-lead authors Gabe Schamberg and Marcus Badgeley. “One can think of our goal as being analogous to an airplane’s autopilot, where the captain is always in the cockpit paying attention,” says Schamberg, a former MIT postdoc who is also the study’s corresponding author.