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

摘要 人工智能和机器学习（AI/ML）的应用正在迅速扩大， 放射肿瘤学数据采集的大规模和应用范围使患者的期望值变得紧张 以及医学和公共卫生的伦理规范。目前的监管制度难以跟上 人工智能/机器学习和当地卫生系统的快速发展在采用和 对内部或商业可用的AI/ML解决方案进行质量保证和审查。总体上 AI/ML的快速扩展将受益于测量患者态度和体验的能力， 使基于证据的最佳做法，以解决医疗和公共卫生伦理问题，如信任， 平等、保证和自主、有益和不有害的生物伦理原则。在 父R 01，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。（联邦发展局） 分类系统。拟议补充项目的目标是扩大这些努力， 评估患者的价值观、态度、关注点和信任度，为更好地服务于人民的政策提供信息， 机构职能体系具体来说，我们建议制定有效的措施，病人的态度和信念的关键 生物医学和公共卫生伦理原则和问题，如自主权，善行，非maleficence， 信任、公平和保证，因为它们与使用AI/ML的预期利益和舒适度有关， 放射肿瘤学这些伦理问题是多方面的，复杂的，相互关联的，并依赖于上下文。 因此，我们的验证程序将包括结构方程模型（目标2），它将捕获 衡量复杂主题的变量之间的潜在关系，并将为解释提供信息， 使用这些措施。为了研究背景如何与伦理价值相关联的问题，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，治疗），以及 合成图像的生成（例如，使用MRI数据生成CT图像）。

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).