# Public trust of artificial intelligence in the precision CDS health ecosystem

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $743,706

## Abstract

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.

## Key facts

- **NIH application ID:** 10459231
- **Project number:** 5R01EB030492-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Jodyn Elizabeth Platt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $743,706
- **Award type:** 5
- **Project period:** 2021-08-02 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10459231

## Citation

> US National Institutes of Health, RePORTER application 10459231, Public trust of artificial intelligence in the precision CDS health ecosystem (5R01EB030492-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10459231. Licensed CC0.

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