机器学习POCUS采用和全系统人工智能实施的临床障碍(COMPASS-AI调查)。

IF 3.4 Q2 Medicine
Adrian Wong, Nurul Liana Roslan, Rory McDonald, Julina Noor, Sam Hutchings, Pradeep D'Costa, Gabriele Via, Francesco Corradi
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引用次数: 0

摘要

背景:即时超声(POCUS)已成为各种医学专业不可或缺的设备。人工智能(AI)和机器学习(ML)的集成有望进一步增强POCUS的能力。然而,缺乏对医疗保健专业人员对这种集成的看法的全面理解。目的:本研究旨在调查医疗保健专业人员在POCUS中对AI的全球认知、熟悉度和采用情况。方法:对参与POCUS的医疗保健专业人员进行了一项国际性的、基于网络的调查。调查工具包括人口统计、对人工智能的熟悉程度、感知效用、障碍(技术、培训、信任、工作流程、法律/道德)以及对人工智能辅助POCUS的总体看法。采用描述性统计、频率分布和分组比较(使用卡方/Fisher精确检验和t检验/Mann-Whitney U检验)对数据进行分析。结果:本研究调查了1154名医疗保健专业人员在护理点超声中实施人工智能的感知障碍。尽管普遍热情高涨,81.1%的受访者表示同意或强烈同意,但仍发现了重大障碍。最常被提及的最大障碍是培训和教育(27.1%)和临床验证和证据(17.5%)。分析还显示,对特定障碍的看法因人口因素而有很大差异,包括执业地区、医学专业和多年的医疗保健经验。结论:这项新颖的全球调查为POCUS对人工智能的认知和采用提供了重要的见解。研究结果突出了相当大的热情以及关键的挑战,主要涉及培训、验证、指导和支持。解决这些障碍对于在POCUS中负责任和有效地实施人工智能至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey).

Background: Point-of-care ultrasound (POCUS) has become indispensable in various medical specialties. The integration of artificial intelligence (AI) and machine learning (ML) holds significant promise to enhance POCUS capabilities further. However, a comprehensive understanding of healthcare professionals' perspectives on this integration is lacking.

Objective: This study aimed to investigate the global perceptions, familiarity, and adoption of AI in POCUS among healthcare professionals.

Methods: An international, web-based survey was conducted among healthcare professionals involved in POCUS. The survey instrument included sections on demographics, familiarity with AI, perceived utility, barriers (technological, training, trust, workflow, legal/ethical), and overall perceptions regarding AI-assisted POCUS. The data was analysed by descriptive statistics, frequency distributions, and group comparisons (using chi-square/Fisher's exact test and t-test/Mann-Whitney U test).

Results: This study surveyed 1154 healthcare professionals on perceived barriers to implementing AI in point-of-care ultrasound. Despite general enthusiasm, with 81.1% of respondents expressing agreement or strong agreement, significant barriers were identified. The most frequently cited single greatest barriers were Training & Education (27.1%) and Clinical Validation & Evidence (17.5%). Analysis also revealed that perceptions of specific barriers vary significantly based on demographic factors, including region of practice, medical specialty, and years of healthcare experience.

Conclusion: This novel global survey provides critical insights into the perceptions and adoption of AI in POCUS. Findings highlight considerable enthusiasm alongside crucial challenges, primarily concerning training, validation, guidelines, and support. Addressing these barriers is essential for the responsible and effective implementation of AI in POCUS.

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来源期刊
Ultrasound Journal
Ultrasound Journal Health Professions-Radiological and Ultrasound Technology
CiteScore
6.80
自引率
2.90%
发文量
45
审稿时长
22 weeks
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