人工智能在麻醉学和围术期医学中的优势-劣势-机会-威胁分析。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2024-02-20 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1316931
Henry J Paiste, Ryan C Godwin, Andrew D Smith, Dan E Berkowitz, Ryan L Melvin
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)在麻醉学和围术期医学中的应用正迅速成为临床实践的主流。麻醉学是一个数据丰富的医学专科,它整合了大量患者特定信息。围术期医学应用人工智能和 ML 的时机已经成熟,可促进精准医疗和预测评估的数据综合。新兴人工智能模型的例子包括协助评估麻醉深度和调控麻醉给药、事件和风险预测、超声引导、疼痛管理和手术室后勤的模型。人工智能和人工智能支持大规模分析围手术期综合数据,并能评估模式,以提供最佳的特定患者护理。通过探讨这项技术的优势和局限性,我们为评估在各种麻醉工作流程中采用人工智能模型提供了考虑基础。本报告分析了人工智能和 ML 在麻醉学和围术期医学中的应用,探讨了当前的形势,以便更好地了解这些工具的优势、劣势、机会和威胁 (SWOT)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine.

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

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CiteScore
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