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

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $302,459

## Abstract

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).

## Key facts

- **NIH application ID:** 10598371
- **Project number:** 3R01EB030492-02S1
- **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:** $302,459
- **Award type:** 3
- **Project period:** 2021-08-02 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598371

## Citation

> US National Institutes of Health, RePORTER application 10598371, Public trust of artificial intelligence in the precision CDS health ecosystem - Administrative Supplement (3R01EB030492-02S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10598371. Licensed CC0.

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