摘要
一位新闻记者-机器人与机器学习的新闻编辑-每日新闻-关于人工智能的研究结果在一份新的报告中讨论。根据NewsRx编辑在蒙特利尔大学的新闻报道，研究表明，“人工智能(AI)技术有望‘彻底改变’医疗保健。”新闻记者从蒙特利尔大学的研究中得到一句话：“然而，尽管他们做出了承诺，他们在医疗机构和系统中的整合仍然有限。本研究的目的是探索和了解他们在加拿大一家领先的学术医院整合的系统性挑战和影响。对29名关注组织内大量人工智能技术整合的利益相关者进行了半结构化访谈（如经理、临床医生、研究人员、患者和其他相关人员）。技术提供者)。使用不采用、放弃、扩大、传播、可持续性(NASSS)框架收集和分析数据。在有利因素和条件中，我们的研究结果强调：支持性的组织崇拜和领导导致连贯的组织创新叙述；高级管理层和一线团队之间的相互信任和透明沟通；冠军、翻译和翻译的存在。制约因素和障碍包括：对人工智能技术价值的定义和衡量这种价值的方法的对比；缺乏真实生活和基于背景的证据；患者的数字和健康素养能力不同；组织动态、临床和行政流程、基础设施之间的错位。和人工智能技术；缺乏涵盖实施、适应和所需专业知识的资金机制；实践变化、新专业知识发展和专业认同带来的挑战；缺乏官方专业人员、报销、和保险指南；缺乏上市前和上市后的法律和治理框架；人工智能技术的业务和融资模式的多样性；投资者的优先事项与医疗机构和系统的需求和期望之间的错位。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of the Uni versity of Montreal by NewsRx editors, research stated, "Artificial intelligence (AI) technologies are expected to ‘revolutionise' healthcare." The news journalists obtained a quote from the research from University of Montr eal: "However, despite their promises, their integration within healthcare organ isations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital. Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technolog ies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. Among enabling factors and conditions, our findings highlight: a supportive organisational cult ure and leadership leading to a coherent organisational innovation narrative; mu tual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI abl e to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definition s of the value of AI technologies and ways to measure such value; lack of real-l ife and context-based evidence; varying patients' digital and health literacy ca pacities; misalignments between organisational dynamics, clinical and administra tive processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges ari sing from practice change, new expertise development, and professional identitie s; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of t he business and financing models for AI technologies; and misalignments between investors' priorities and the needs and expectations of healthcare organisations and systems."
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