Garvey Kim V, Thomas Craig Kelly Jean, Russell Regina, Novak Laurie L, Moore Don, Miller Bonnie M
Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, TN, United States.
Department of Anesthesiology, School of Medicine, Vanderbilt University, Nashville, TN, United States.
JMIR Med Inform. 2022 Nov 16;10(11):e37478. doi: 10.2196/37478.
The use of artificial intelligence (AI)-based tools in the care of individual patients and patient populations is rapidly expanding.
The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings.
A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States.
The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians.
While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.
基于人工智能（AI）的工具在个体患者及患者群体护理中的应用正在迅速扩展。
本文旨在系统识别关于在临床环境中使用人工智能所需的医疗服务提供者能力的研究。
进行了一项范围综述，以识别2009年1月1日至2020年5月1日期间发表在MEDLINE、CINAHL和Cochrane图书馆数据库中的文章，使用与医疗保健专业人员（如医学、护理和药学）及其在临床教育各阶段的专业发展、临床实践所有环境中的基于人工智能的工具以及能力和表现的专业教育领域相关的术语进行搜索查询。限定条件为英语语言、有摘要的人体研究以及美国的研究环境。
搜索共识别出3476条记录，其中4条符合纳入标准。这些研究描述了人工智能在临床实践中的应用，并测量了临床医生能力的至少一个方面。虽然许多研究测量了基于人工智能的工具的性能，但只有4项研究从理解和有效使用正在测试的新工具所需的知识、技能或态度方面测量了临床医生的表现。这4篇文章主要关注人工智能通过改善信息流和显示来增强患者护理和临床决策的能力，特别是对医生而言。
虽然识别出了许多研究人工智能技术在医疗保健中潜在有效性的研究，但很少有研究涉及临床医生有效使用这些技术所需的特定能力。这凸显了一个关键差距。
