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: 10459231
• 负责人: Jodyn Elizabeth Platt
• 金额: $ 74.37万
• 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/10459231
• 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 人工智能增强型临床决策支持 (AI-CDS) 是一个价值数十亿美元的不断增长的产业 利用广泛的临床、基因组、社交、地理、基于网络和可穿戴设备数据 健康结果的改善被广泛地定义为“精准健康”。 数据以数量、速度、准确性、多样性和价值为特征，以 AI-CDS 形式呈现的“大知识”是 变得越来越普遍（数量）、快速发展（速度）、可用于广泛的医学领域 领域（各种），基于反映个人健康状况的广泛来源的数据 人口（真实性），并专注于降低成本和促进更好的健康结果（价值）。 CDS 的政策范式，包括是否将其归类为医疗器械，并不是为适应性而设计的 人工智能技术。患者和提供者没有合理的方法来辨别这些“黑色”。 “盒子”技术的运作或其创新政策（例如产品标签标准）。 解决这些问题可能需要直接向消费​​者进行宣传和沟通，以确保公众 事实上，公众对 AI-CDS 的信任已被确定为 AI-CDS 的首要任务。 CDS 大知识生态系统由美国国家医学院、NIH、FDA 和 OMB 等机构组成。 鉴于与透明度相关的一系列关键道德和政策考虑因素，信任尤为重要， AI-CDS 的隐私、非恶意、公平、责任和效用 在我们拟议研究的目标 1 中，我们将。 衡量公众当前对 AI-CDS 精准健康的信任度，并评估 (a) 其与公众的关系 对隐私、公平、非恶意、责任和效用的期望和担忧，以及 (b) 如何实现 受到政策和实践的影响，例如标签或认证。在目标 2 中，我们将使用协商。 民主方法和专家访谈，旨在直接为解决问题的政策和标准提供信息 AI-CDS 的感知风险，在目标 3 中，我们建议开发一个产品信息标签，既能 为患者和提供者提高 AI-CDS 信息的透明度和可访问性。 AI-CDS 的持续接受和采用取决于公众的信任，我们的提案规定 采用以研究为重点和基于证据的方法，将公众参与纳入新兴市场 国家标准。