# SCH: Generative Imaging Models for Verifying and Explaining Machine Learning Systems in Healthcare

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of Virginia Main Campus (VA) · $1,000,000

## Abstract

Artificial intelligence - in particular, deep learning - is rapidly being developed for healthcare systems with great potential to improve disease diagnosis, treatment planning, and patient monitoring. However, the translation of these powerful models from research and development to everyday clinical use is being held back by the lack of trustworthiness in these systems. This project will explore strategies and develop methods for ensuring the robustness of deep-learning models in healthcare applications. A major obstacle to guaranteeing the behavior of deep-learning systems in healthcare is the wide variability in data across different healthcare sites, including a range of medical-imaging devices, data-collection protocols, and patient demographics. This can lead to data inputs to the system that are significantly different in nature from the data on which it was trained. To address this issue, we propose to develop robustness audits that assess how well a healthcare deep-learning system tailored to a specific site will operate at another site. Broader-impact aspects of the work include the potential to significantly and widely improve the effectiveness of deep learning in practical healthcare applications. Additionally, an array of educational and outreach activities are planned.

The first goal of this project is to develop robustness audits using synthetic data that provide full coverage of test cases simulating conditions at a target healthcare site. This will be don

## Key facts

- **NSF award ID:** 2501059
- **Awardee organization:** University of Virginia Main Campus (VA)
- **SAM.gov UEI:** JJG6HU8PA4S5
- **PI:** Preston T Fletcher
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** SIGNAL PROCESSING, Smart and Connected Health
- **Estimated total:** $1,000,000
- **Funds obligated:** $1,000,000
- **Transaction type:** Standard Grant
- **Period:** 09/15/2025 → 08/31/2029

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2501059

## Citation

> US National Science Foundation, Award 2501059, SCH: Generative Imaging Models for Verifying and Explaining Machine Learning Systems in Healthcare. Retrieved via AI Analytics 2026-06-06 from https://api.ai-analytics.org/grant/nsf/2501059. Licensed CC0.

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