# CAREER: Making Domain-Specific AI Models Steerable by Leveraging Foundational Models

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Trustees of Boston University (MA) · $499,997

## Abstract

Artificial intelligence (AI) tools are increasingly used in healthcare systems to help diagnose diseases from medical images such as Computerized Tomography (CT) scans and mammograms. While these systems can be highly accurate, they often learn unintended patterns, such as utilizing  hospital-specific markings rather than markers of disease. This can lead to uneven or unsafe performance. Compounding this problem, most AI models are “black boxes,” offering little insight into how decisions are made or why mistakes occur. Identifying the source of mistakes is  challenging for AI developers due to the knowledge gap between AI scientists and clinicians, and rectifying those mistakes is difficult for doctors because of the inherent complexity of the AI systems. This project develops new methods to make these systems more transparent and adjustable, allowing clinicians and researchers to understand, diagnose, and correct AI errors without needing to rebuild the models entirely. For instance, if a breast cancer risk model performs better on one group of patients than another, the new tools can help identify the cause and allow clinicians to intervene. In addition to improving fairness and reliability in medical AI, this project will also advance education and workforce development by involving students in interdisciplinary research at the intersection of medicine, computer science, and engineering. To reach these objectives, this project utilizes and enhances innovative computationa

## Key facts

- **NSF award ID:** 2443167
- **Awardee organization:** Trustees of Boston University (MA)
- **SAM.gov UEI:** THL6A6JLE1S7
- **PI:** Kayhan batmanghelich
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** AI-Supported Learning, AI Education/Workforce Develop, BIOTECHNOLOGY - INFRASTRUCTURE, CAREER-Faculty Erly Career Dev
- **Estimated total:** $499,997
- **Funds obligated:** $499,997
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2443167

## Citation

> US National Science Foundation, Award 2443167, CAREER: Making Domain-Specific AI Models Steerable by Leveraging Foundational Models. Retrieved via AI Analytics 2026-06-06 from https://api.ai-analytics.org/grant/nsf/2443167. Licensed CC0.

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