# SCH: Fundamental Limits of Fair and Privacy-Preserving Healthcare Models

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Michigan State University (MI) · $1,000,000

## Abstract

Artificial intelligence (AI)-based systems play a critical role in modern healthcare, from diagnosing diseases through medical imaging to managing patient care efficiently in emergency rooms. However, these systems can inadvertently perpetuate inaccuracies arising from historical or manually curated data, reducing effectiveness for certain patient groups or unintentionally exposing sensitive patient information. To address these critical challenges, this project will advance our understanding of the fundamental trade-offs among predictive accuracy, robustness across patient populations, and patient privacy in AI-driven healthcare. Insights from this project will directly benefit society by promoting safer, more reliable, and trustworthy healthcare technologies, ultimately advancing national goals in public health and welfare. Additionally, the research will support the education and training of future researchers, including graduate students, and inspire K-12 students to pursue careers in STEM fields.

The project has two research objectives. First, it aims to provide a rigorous theoretical understanding and practical evaluation of performance trade-offs among predictive accuracy, robustness across patient populations, and privacy in healthcare predictive models, particularly those used for diagnosing skin and soft tissue infections. These trade-offs will be analyzed both in centrally trained models and personalized models tailored to specific patient characteristics. Secon

## Key facts

- **NSF award ID:** 2500983
- **Awardee organization:** Michigan State University (MI)
- **SAM.gov UEI:** R28EKN92ZTZ9
- **PI:** Vishnu Boddeti
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Smart and Connected Health
- **Estimated total:** $1,000,000
- **Funds obligated:** $1,000,000
- **Transaction type:** Standard Grant
- **Period:** 08/15/2025 → 07/31/2029

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2500983

## Citation

> US National Science Foundation, Award 2500983, SCH: Fundamental Limits of Fair and Privacy-Preserving Healthcare Models. Retrieved via AI Analytics 2026-06-06 from https://api.ai-analytics.org/grant/nsf/2500983. Licensed CC0.

---

*[NSF Awards dataset](/datasets/nsf-awards) · CC0 1.0*