Public trust of artificial intelligence in the precision CDS health ecosystem

摘要 人工智能增强的临床决策支持(AI-CDS)是一个价值数十亿美元的增长行业 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限定在“精确健康”一词之下。由大型企业提供支持 以数量、速度、准确性、多样性和价值为特征的数据，以AI-CDS的形式表现出来的大知识是 变得越来越无处不在(数量)，快速发展(速度)，可用于广泛的医疗 领域(种类)，基于来自广泛来源的数据，反映个人和 人口(准确性)，并侧重于降低成本和促进更好的健康结果(价值)。当前 CDS的政策范例，包括是否将其归类为医疗设备，并不是为自适应而设计的 人工智能技术。患者和提供者没有合理的方法来辨别这些“黑色” BOX“技术的运行或其准确性。创新政策(例如，产品标签标准) 解决这些问题可能需要直接的消费者接触和沟通，以确保公众 信任不断增长的AI-CDS领域。事实上，公众对AI-CDS的信任已被确定为AI的首要任务- CDS大型知识生态系统由国家医学院、NIH、FDA和OMB等机构提供。 鉴于与透明度有关的一系列重要的道德和政策考虑，信任尤为突出， AI-CDS的隐私、非恶意、公平、问责和效用。在我们拟议研究的目标1中，我们将 衡量公众目前对AI-CDS在精确健康方面的信任，并评估(A)其与公众的关系 对隐私、公平、非恶意、责任和公用事业的期望和关注，以及(B)如何 受到政策和实践的影响，如标签或认证。在目标2中我们将使用深思熟虑 民主方法和专家访谈，旨在直接向政策和标准提供信息，以解决 感知人工智能-CDS的风险，在目标3中，我们建议开发一种产品信息标签，该标签将既 提高患者和提供者关于AI-CDS的信息的透明度和可及性。这个 继续接受和采用AI-CDS是建立在公众信任的基础上的，我们的建议提供了 以研究为重点、以证据为基础的方法，将公众参与纳入新兴市场 国家标准。

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.