# A framework to quantify and incorporate uncertainty for ethical application of AI-based quantitative imaging in clinical decision making

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2022 · $314,807

## Abstract

Project Summary: Quantitative imaging (QI), where a numerical/statistical feature is computed from a patient
image, is emerging as an important tool for diagnosis and therapy planning. Artificial intelligence (AI)-based QI
tools are showing significant promise in this area. However, the measured quantitative value from these tools
may also suffer from uncertainty due to various reasons such as limited training data, inaccurate ground truth,
mismatch between training and test sets. For ethical application of AI-based QI tools, this uncertainty should be
quantified and then incorporated in the clinical decision-making process. This is necessary for the ethical
application of these tools, an inference that also emerged from a survey conducted by us across patient
advocates (Birch et al, Nature Medicine 2022). Towards addressing this goal, in this proposal, we first propose
to develop a novel no-gold-standard method to quantify uncertainty of AI-based QI tools using patient data.
Existing uncertainty quantification techniques have mainly been developed for detection tasks, and typically
require availability of gold standard. In contrast, the proposed technique will be developed for quantification tasks
and not require any gold standard quantitative value. Next, to incorporate the uncertainty of the AI-based QI tool,
we propose to propose to develop a questionnaire that will elicit the patient’s risk-value profiles towards
treatments. For example, if an AI-based QI tool outputs a quantitative value that indicates aggressive therapy,
but with high uncertainty, some patients may be risk averse and prefer to assign high weight to the uncertainty
value, while other patients may value the benefits of the treatment and thus assign less weight to that uncertainty.
To incorporate these patient preferences, we propose to develop a questionnaire that will elicit the patient’s risk-
value profiles. This project will advance on the ongoing activities of our current R01 award on no-gold-standard
evaluation of QI methods, extending that project in the context of uncertainty quantification, and thus enabling
the use of our tools for not just evaluation, but generating personalized recommendations for each patient. The
methods will be developed in the context of the highly significant clinical question of guiding therapy response in
patients with stage III non-small cell lung cancer (NSCLC). Answering this question will help address a critical,
urgent, and unmet need for strategies to personalize the treatment of NSCLC, a disease with high morbidity and
mortality rates. A highly multi-disciplinary team consisting of imaging scientist with expertise in AI, AI ethicists,
oncologist, and nuclear-medicine physician have been assembled for this study. This supplement is directly
responsive to NOT-OD-22-065 in terms of developing a framework for ethical clinical use of AI. The project will
also strengthen the impact of tools we are developing in the parent R01 by using the...

## Key facts

- **NIH application ID:** 10599754
- **Project number:** 3R01EB031051-02S1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Abhinav K Jha
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $314,807
- **Award type:** 3
- **Project period:** 2021-04-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10599754

## Citation

> US National Institutes of Health, RePORTER application 10599754, A framework to quantify and incorporate uncertainty for ethical application of AI-based quantitative imaging in clinical decision making (3R01EB031051-02S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10599754. Licensed CC0.

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