Public trust of artificial intelligence in the precision CDS health ecosystem

精准CDS健康生态系统中人工智能的公众信任

• 批准号: 10092723
• 负责人: Jodyn Elizabeth Platt
• 金额: $ 70.08万
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10092723
• 关键词: Academy; Accountability; Address; Adoption; Adult; Affect; Age; Algorithmic Software; Apple; Applications Grants; Artificial Intelligence; Attitude; Big Data; British; Caring; Case Study; Centers for Disease Control and Prevention (U.S.); Certification; Clinical; Collaborations; Communication; Communities; Competence; Computer software; Data; Ecosystem; Ensure; Epidemiology; Ethics; Generations; Genomics; Geography; Goals; Growth; Guidelines; Health; Health Policy; Health Professional; Health Sciences; Health system; Heart Diseases; Individual; Industry; Institutes; Institution; Interview; Investigation; Investments; Knowledge; Label; Leadership; Learning; Measures; Medical; Medical Device; Medicine; Melissa; Methods; Michigan; Nonmaleficence; Notification; Online Systems; Outcome; Participant; Patients; Perception; Policies; Population; Precision Health; Privacy; Privatization; Probability; Product Labeling; Provider; Public Health; Public Participation; Recommendation; Research; Risk; Sampling; Security; Source; Surveys; System; Technology; Trust; United States Agency for Healthcare Research and Quality; United States Food and Drug Administration; United States National Institutes of Health; Universities; Vendor; Women' s Health; algorithm development; base; care providers; clinical decision support; cost; data sharing; deliberative democracy; design; digital; ethical legal social implication; evidence base; expectation; follow-up; health data; health management; improved; innovation; learning algorithm; learning network; longitudinal analysis; machine learning algorithm; outreach; patient oriented; patient population; patient portal; point of care; precision oncology; preference; public trust; social; wearable device

Abstract Artificial intelligence-enhanced Clinical Decision Support (AI-CDS) is a growing multibillion-dollar industry leveraging a wide range of clinical, genomic, social, geographical, web-based, and wearable device data for improvements in health outcomes broadly circumscribed under the term “precision health.” Powered by Big Data, characterized by volume, velocity, veracity, variety, and value, “big knowledge” in the form of AI-CDS is becoming increasingly ubiquitous (volume), rapidly developing (velocity), available to a wide range of medical fields (variety), based on data from a wide range of sources that reflects the health of individuals and populations (veracity), and focused on lowering costs and promoting better health outcomes (value). Current policy paradigms for CDS, including whether to classify it as a medical device, are not designed for adaptive artificial intelligence technologies. Patients and providers have no reasonable way to discern how these “black box” technologies operate or their accuracy. Innovative policies (e.g. standards in product labeling) that address these concerns are likely to require direct consumer outreach and communications to ensure public trust in the growing AI-CDS field. Indeed, public trust in AI-CDS has been identified as a top priority for the AI- CDS big knowledge ecosystem by the National Academy of Medicine, NIH, FDA, and OMB, among others. Trust is particularly salient given the range of critical ethical and policy considerations related to transparency, privacy, non-maleficence, equity, accountability, and utility of AI-CDS. In Aim 1 of our proposed study, we will measure the public's current trust in AI-CDS for precision health and assess (a) its relationship to the public's expectations and concerns about privacy, equity, non-maleficence, responsibility, and utility and (b) how it may be affected by policies and practices, such as labeling or certification. In Aim 2 we will use deliberative democracy methods and expert interviews, designed to directly inform policy and standards that address perceived risks of AI-CDS and in Aim 3 we propose to develop a product information label that would both increase transparency and accessibility of information about AI-CDS for patients and providers. The continued acceptance and adoption of AI-CDS is predicated on public trust and our proposal provides a research-focused and evidence-based approach to incorporating public participation into emerging national standards.

抽象的 人工智能增强临床决策支持（AI-CD）是一个不断增长的数十亿美元的行业 利用各种临床，基因组，社会，地理，基于网络和可穿戴设备数据 根据“精度健康”一词的广泛限制健康结果的改善。由大型提供支持 数据以卷，速度，真实性，多样性和价值为特征，以AI-CD的形式为“大知识” 变得越来越无处不在（体积），迅速发展（速度），可用于广泛的医疗 田地（品种），基于来自各种来源的数据，这些数据反映了个人的健康和 人口（真实性），专注于降低成本并促进更好的健康成果（价值）。当前的 CD的策略范例，包括是否将其分类为医疗设备，并不是专为自适应而设计的 人工智能技术。患者和提供者没有合理的方法来辨别这些黑人如何 盒子”技术运行或其准确性。创新政策（例如，产品标签中的标准） 解决这些问题可能需要直接消费者外展和沟通以确保公众 信任不断增长的AI-CD领域。实际上，公众对AI-CD的信任已被确定为AI-的首要任务 美国国家医学院，NIH，FDA和OMB等人的CD大知识生态系统。 鉴于与透明度相关的一系列批判性道德和政策考虑范围，信任尤其重要， AI-CD的隐私，非遗憾，权益，问责制和效用。在我们提出的研究的目标1中，我们将 衡量公众目前对AI-CD的信任，以进行精确健康和评估（a）与公众的关系 对隐私，公平，非遗憾，责任和实用性的期望和关注以及（b）如何 受政策和实践的影响，例如标签或认证。在AIM 2中，我们将使用审议 民主方法和专家访谈，旨在直接告知政策和标准 AI-CD的感知风险，在AIM 3中，我们建议开发一个产品信息标签 增加有关患者和提供者的有关AI-CD的信息的透明度和可访问性。这 预测公共信托的预测和采用AI-CD的持续接受和采用 一种以研究为中心的循证方法，将公众参与纳入新兴 国家标准。