Artificial intelligence in ultrasound-guided regional anesthesia: bridging the gap between potential and practice: a narrative review.

IF 3.6
Anesthesia and pain medicine Pub Date : 2025-10-01 Epub Date: 2025-10-31 DOI:10.17085/apm.25354
Yumin Jo, Sujin Baek, Donghyeon Baek, Chahyun Oh, Dongheon Lee, Boohwi Hong
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引用次数: 0

Abstract

Ultrasound-guided regional anesthesia (UGRA) offers substantial benefits in perioperative pain management; however, it remains underutilized because of technical complexity and training demands. Assistive artificial intelligence (AI) has emerged as a promising solution to support UGRA by enhancing anatomical recognition, procedural accuracy, and user confidence. This narrative review outlines the AI development pipeline for nerve visualization, describes available commercial tools, and summarizes clinical evidence. Although these technologies have the potential to democratize UGRA and reduce interoperator variability, limitations remain, including data bias, narrow anatomical coverage, and lack of outcome-based validation. Future efforts should focus on standardized evaluation, clinician-centered design, and rigorous clinical trials to ensure safe and effective integration of AI into UGRA practice.

Abstract Image

Abstract Image

Abstract Image

超声引导区域麻醉中的人工智能:弥合潜力与实践之间的差距:叙述性回顾。
超声引导区域麻醉(UGRA)在围手术期疼痛管理中提供了实质性的好处;但是,由于技术的复杂性和培训的需要,它仍然没有得到充分利用。辅助人工智能(AI)通过增强解剖识别、程序准确性和用户信心,已成为支持UGRA的有前途的解决方案。这篇叙述性综述概述了神经可视化的人工智能开发管道,描述了可用的商业工具,并总结了临床证据。尽管这些技术有可能使UGRA大众化,减少操作者之间的可变性,但仍然存在局限性,包括数据偏差、狭窄的解剖覆盖范围和缺乏基于结果的验证。未来的工作应侧重于标准化评估、以临床为中心的设计和严格的临床试验,以确保人工智能安全有效地融入UGRA实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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