Natural Language Processing and Automated Speech Recognition to Identify Older Adults with Cognitive Impairment Supplement

研究和/或开发人工智能(AI)健康技术的现有伦理框架 护理应用主要是概念性的，缺乏来自患者和临床医生(包括患者)的关键见解 来自种族和伦理少数群体。建立在正在开发的家长研究(R01 AG066471)的基础上 初级保健中认知障碍筛查的自动化技术，我们计划使用定性方法来 从不同的患者群体和医生那里收集观点，并将这些数据与概念相结合 以及来自文献中关于医疗保健领域人工智能研究的伦理挑战的数据。虽然父母的研究是具体的 对于认知障碍筛查，我们预计该项目的活动将产生一个框架， 更广泛的应用。拟议的项目利用了一个多学科临床医生团队的合作， 计算机科学家、少数族裔健康专家和生物伦理学。具体目标是(1)识别和 描述患者的看法和关切，包括那些来自代表性不足的少数群体的患者， 和临床医生关于在门诊环境下自动筛查认知障碍的人工智能方法； 以及(2)将这些知识与人工智能研究和医疗保健筛查的现有伦理框架相结合，以 为医疗保健环境中人工智能研究的伦理行为制定更全面的伦理框架。 我们将首先整合几个关于人工智能研究和医疗保健筛查和使用的既定道德框架 由此产生的初步框架，为定性数据收集提供信息。接下来，我们将进行定性的 采访来自不同背景的患者，了解他们对自动筛查和 临床护理方面的人工智能研究(例如，知情同意、结果披露)，以及与临床医生的重点小组 他们对可能阻碍收养的道德挑战的看法。同时，我们将进行棱镜检查 审查以确定其他相关框架和研究，并以交互方式改进我们的定性数据 收集。最后，我们将把定性数据与现有文献和框架中的概念相结合，以 建立一个比目前公布的框架更全面和更具包容性的框架。在这种方式下， 工作将为人工智能驱动的自动认知障碍筛查和人工智能研究的伦理行为提供信息 对其他情况进行研究。本附录响应NOT-OD-22-065，支持新的 在人工智能研究伦理方面的合作，并开发探索和解决伦理问题的通用方法 在整个人工智能研究周期中产生影响，特别是通过推进人工智能研究伦理框架。

Existing ethical frameworks for the study and or development of artificial intelligence (AI) technology for health care applications are largely conceptual, lacking critical insights from patients and clinicians, including patients from racial and ethic minority groups. Building on the Parent Study (R01 AG066471), which is developing automated techniques for cognitive impairment screening in primary care, we plan to use qualitative methods to collect perspectives from diverse patient groups, and from physicians, and integrate these data with concepts and data from the literature on ethical challenges of AI research in healthcare. While the Parent Study is specific to cognitive impairment screening, we anticipate that the activities of this project will generate a framework with broader applications. The proposed project harnesses a collaboration of a multi-disciplinary team of clinicians, computer scientists, experts in minority health, and bioethics. The specific aims are (1) to identify and characterize the perceptions and concerns of patients, including those from underrepresented minority groups, and clinicians about AI methods for automated screening for cognitive impairment in outpatient clinical settings; and (2) to integrate this knowledge with existing ethical frameworks of AI research and healthcare screening to develop a more comprehensive ethical framework for the ethical conduct of AI research in healthcare settings. We will first integrate several established ethical frameworks on AI research and healthcare screening and use the resultant preliminary framework to inform qualitative data collection. Next, we will conduct qualitative interviews with patients from diverse backgrounds to understand their perspectives on automated screening and AI research in clinical care (e.g., informed consent, disclosure of results), and focus groups with clinicians for their views on ethical challenges that could hinder adoption. In parallel, we will conduct a PRISMA-Scoping review to identify additional relevant frameworks and research, and interactively refine our qualitative data collection. Finally, we will integrate the qualitative data with concepts from existing literature and frameworks to establish a more comprehensive and inclusive framework than those currently in publication. In this fashion, the work will inform the ethical conduct of research on AI-driven automated cognitive impairment screening and AI research for other conditions. This supplement is responsive to NOT-OD-22-065 by supporting a new collaboration on AI research ethics and developing generalizable methods of exploring and addressing ethical impacts throughout the AI research cycle, specifically through advancing AI research ethical frameworks.