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

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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).