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) 应用正在迅速扩展，例如 放射肿瘤学。数据采集​​的规模和应用范围极大地影响了患者的期望 以及医学和公共卫生的道德范式。当前的监管制度难以跟上 人工智能/机器学习的快速发展速度和地方卫生系统在采用和应用方面的能力差异很大 对内部或商用人工智能/机器学习解决方案进行质量保证和审查。一般来说， 人工智能/机器学习的快速扩展将受益于衡量患者态度和体验的能力 实现基于证据的最佳实践，以解决医疗和公共卫生道德问题，例如信任、 公平、保证以及自主、仁慈和非恶意的生物伦理原则。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。拟议补充项目的目标是将这些努力扩大到 评估患者的价值观、态度、担忧和信任，以制定更好地服务于人民和患者的政策 机构。具体来说，我们建议制定有效的措施来衡量患者对关键问题的态度和信念 生物医学和公共卫生伦理原则和问题，如自主、仁慈、非恶意、 信任、公平和保证，因为它们与使用人工智能/机器学习的预期收益和舒适度相关 放射肿瘤学。这些伦理问题是多维的、复杂的、相互关联的并且依赖于背景。 因此，我们的验证程序将包括结构方程建模（目标 2），它将捕获 测量复杂主题的变量之间的潜在关系并将告知解释和 使用措施。为了研究背景如何与道德价值观相关联的问题，我们将 检查当前放射肿瘤学用例中的这些问题：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，疗法），以及 生成合成图像（例如，使用 MRI 数据生成 CT 图像）。