Public trust of artificial intelligence in the precision CDS health ecosystem - Administrative Supplement

精准CDS健康生态系统中人工智能的公众信任-行政补充

• 批准号: 10598371
• 负责人: Jodyn Elizabeth Platt
• 金额: $ 30.25万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-02 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10598371
• 关键词: Accountability; Address; Administrative Supplement; Adopted; Artificial Intelligence; Attention; Attitude; Automobile Driving; Belief; Beneficence; Bioethical Issues; Bioethics; Cancer Patient; Clinical; Complex; Computer software; Data; Data Scientist; Development; Diagnostic; Ecosystem; Ensure; Equation; Equilibrium; Ethical Issues; Ethicists; Ethics; FAIR principles; Fibrosis; Generations; Goals; Health; Health Professional; Health system; Image; Indigenous; Informed Consent; Institution; Institutional Racism; Machine Learning; Magnetic Resonance Imaging; Measures; Medical; Medical Device; Medicine; Modeling; Nonmaleficence; Notification; Parents; Patients; Persons; Policies; Privacy; Procedures; Product Labeling; Public Health; Race; Radiation Oncology; Research; Research Personnel; Structural Racism; Surveys; System; TRUST principles; Technology; Testing; Trust; United States Food and Drug Administration; Validation; X-Ray Computed Tomography; authority; clinical decision support; cognitive interview; computerized tools; data acquisition; data tools; dosage; evidence base; experience; improved; machine learning algorithm; machine learning method; outcome prediction; patient expectation; predictive modeling; public health ethics; public trust; quality assurance; trustworthiness

ABSTRACT Artificial Intelligence and Machine Learning (AI/ML) applications are rapidly expanding in fields such as radiation oncology. The grand scale of data acquisition and scope of applications strains patient expectations and ethical paradigms for medicine and public health. Current regulatory regimes struggle to keep pace with the rapid pace of development in AI/ML and local health systems vary widely in their capacity to adopt and conduct quality assurance and review for in-house or commercially available AI/ML solutions. In general, the rapid expansion of AI/ML would benefit from the ability to measure patient attitudes and experiences that would enable evidence-based best practices for addressing medical and public health ethical issues such as trust, equity, and assurance, and bioethical principles of autonomy, beneficence, and non-maleficence. In the Parent R01, we are examining public trust in AI/ML as it applies to clinical decision support use cases. (FDAs) system of categorization. The goal of the proposed Supplemental project is to expand these efforts to assess values, attitudes, concerns, and trust of patients to inform policy that better serves people and institutions. Specifically, we propose to develop validated measures of patient attitudes and beliefs about key biomedical and public health ethical principles and issues such as autonomy, beneficence, non-maleficence, trust, equity, and assurance, as they relate to the expected benefit of and comfort with the use of AI/ML in radiation oncology. These ethical issues are multi-dimensional, complex, interrelated, and reliant on context. Our validation procedures will thus include structural equation modeling (Aim 2), which will capture the underlying relationships between variables that measure complex topics and will inform the interpretation and use of the measures. To examine the question of how context is associated with ethical values, we will examine these issues in current radiation oncology use cases: quality assessment (e.g., verifying dosage), outcome predictive models (e.g., predicting fibrosis), treatment predictive models (e.g., therapies), and generation of synthetic images (e.g., using MRI data to generate CT images).

抽象的 人工智能和机器学习（AI/ML）应用程序在诸如等领域正在迅速扩展 辐射肿瘤学。数据获取和应用程序范围的巨大规模，患者期望 以及医学和公共卫生的道德范例。当前的监管制度难以与 AI/ML和当地卫生系统的发展速度迅速，其采用能力和 对内部或商业上可用的AI/ML解决方案进行质量保证和审查。通常， AI/ML的快速扩展将受益于衡量患者态度和经验的能力 启用基于证据的最佳实践来解决医学和公共卫生的道德问题，例如信任， 公平和保证以及自治，福利和非遗憾的生物伦理原则。在 家长R01，我们正在研究公众对AI/ML的信任，因为它适用于临床决策支持用例。 （FDA） 分类系统。拟议的补充项目的目的是将这些努力扩展到 评估患者的价值，态度，关注和信任，以告知更好地为人民服务的政策和 机构。具体而言，我们建议制定对关键的患者态度和信念的验证措施 生物医学和公共卫生的道德原则以及诸如自治，福利，非遗憾的问题， 信任，公平和保证，与使用AI/ML的预期利益和舒适性有关 辐射肿瘤学。这些道德问题是多维，复杂，相互关联的，并且在上下文上依赖。 因此，我们的验证程序将包括结构方程建模（AIM 2），将捕获 衡量复杂主题的变量之间的基本关系，并将告知解释和 使用这些措施。要研究上下文如何与道德价值相关的问题，我们将 在当前的辐射肿瘤学用例中检查这些问题：质量评估（例如，验证剂量）， 结果预测模型（例如预测纤维化），治疗预测模型（例如，治疗）和 生成合成图像（例如，使用MRI数据生成CT图像）。